├── resources └── teaser.jpg ├── requirements.txt ├── eval.sh ├── LICENSE ├── data_process ├── process.sh ├── deduplicate.sh ├── deduplicate_surfedge.py ├── deduplicate_cad.py ├── process_brep.py └── convert_utils.py ├── eval_config.yaml ├── vae.py ├── train_vae.sh ├── ldm.py ├── sample_points.py ├── train_ldm.sh ├── .gitignore ├── README.md ├── pc_metric.py ├── sample.py ├── dataset.py ├── LICENSE_GPL ├── utils.py └── trainer.py /resources/teaser.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/samxuxiang/BrepGen/HEAD/resources/teaser.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | --extra-index-url https://download.pytorch.org/whl/cu118 2 | torch 3 | torchvision 4 | torchaudio 5 | diffusers[torch]==0.27 6 | transformers 7 | wandb 8 | matplotlib 9 | scipy 10 | trimesh 11 | plyfile 12 | scikit-learn 13 | -------------------------------------------------------------------------------- /eval.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash\ 2 | 3 | # sample surface point cloud 4 | python sample_points.py --in_dir path/to/your/generated/samples/folder --out_dir sampled_pointcloud 5 | 6 | # Evaluate MMD/COV/JSD 7 | python pc_metric.py --fake sampled_pointcloud --real deepcad_test_pcd -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The code, data and the model weights in this repository are not allowed for commercial usage. For research purposes, the terms follow the GPL v3, as in the separate file "LICENSE_GPL". 2 | -- Authors of the paper "BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry". 3 | -------------------------------------------------------------------------------- /data_process/process.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Adjust the timeout time accordingly, memory could explode when loading few very large cad models 4 | 5 | 6 | ### Process DeepCAD data ### 7 | for i in $(seq 0 18) 8 | do 9 | # Call python script with different interval values 10 | timeout 500 python process_brep.py --input path/to/your/abc_step --interval $i --option 'deepcad' 11 | pkill -f '^python process_brep.py' # cleanup after each run 12 | done 13 | 14 | 15 | ### Process ABC data ### 16 | for i in $(seq 0 99) 17 | do 18 | # Call python script with different interval values 19 | timeout 1000 python process_brep.py --input path/to/your/abc_step --interval $i --option 'abc' 20 | pkill -f '^python process_brep.py' # cleanup after each run 21 | done 22 | 23 | 24 | ### Process Furniture data ### 25 | python process_brep.py --input path/to/your/furniture_step --option 'furniture' -------------------------------------------------------------------------------- /data_process/deduplicate.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | ### Dedupliate DeepCAD ### 4 | # Deduplicate repeatd CAD B-rep (LDM training) 5 | python deduplicate_cad.py --data deepcad_parsed --bit 6 --option 'deepcad' 6 | # Deduplicate repeated surface & edge (VAE training) 7 | python deduplicate_surfedge.py --data deepcad_parsed --list deepcad_data_split_6bit.pkl --bit 6 --option 'deepcad' 8 | python deduplicate_surfedge.py --data deepcad_parsed --list deepcad_data_split_6bit.pkl --bit 6 --edge --option 'deepcad' 9 | 10 | 11 | ### Dedupliate ABC ### 12 | # Deduplicate repeatd CAD B-rep (LDM training) 13 | python deduplicate_cad.py --data abc_parsed --bit 6 --option 'abc' 14 | # Deduplicate repeated surface & edge (VAE training) 15 | python deduplicate_surfedge.py --data abc_parsed --list abc_data_split_6bit.pkl --bit 6 --option 'abc' 16 | python deduplicate_surfedge.py --data abc_parsed --list abc_data_split_6bit.pkl --bit 6 --edge --option 'abc' 17 | 18 | 19 | ### Dedupliate Furniture ### 20 | # Deduplicate repeatd CAD B-rep (LDM training) 21 | python deduplicate_cad.py --data furniture_parsed --bit 6 --option 'furniture' 22 | # Deduplicate repeated surface & edge (VAE training) 23 | python deduplicate_surfedge.py --data furniture_parsed --list furniture_data_split_6bit.pkl --bit 6 --option 'furniture' 24 | python deduplicate_surfedge.py --data furniture_parsed --list furniture_data_split_6bit.pkl --bit 6 --edge --option 'furniture' -------------------------------------------------------------------------------- /eval_config.yaml: -------------------------------------------------------------------------------- 1 | abc: 2 | surfpos_weight: abc_ldm_surfpos.pt 3 | surfz_weight: abc_ldm_surfz.pt 4 | edgepos_weight: abc_ldm_edgepos.pt 5 | edgez_weight: abc_ldm_edgez.pt 6 | surfvae_weight: abc_vae_surf.pt 7 | edgevae_weight: abc_vae_edge.pt 8 | save_folder: samples_abc 9 | batch_size: 16 10 | z_threshold: 0.2 11 | bbox_threshold: 0.08 12 | num_surfaces: 50 13 | num_edges: 40 14 | use_cf: False 15 | class_label: [] 16 | 17 | deepcad: 18 | surfpos_weight: deepcad_ldm_surfpos.pt 19 | surfz_weight: deepcad_ldm_surfz.pt 20 | edgepos_weight: deepcad_ldm_edgepos.pt 21 | edgez_weight: deepcad_ldm_edgez.pt 22 | surfvae_weight: deepcad_vae_surf.pt 23 | edgevae_weight: deepcad_vae_edge.pt 24 | save_folder: samples_deepcad 25 | batch_size: 16 26 | z_threshold: 0.2 27 | bbox_threshold: 0.08 28 | num_surfaces: 30 29 | num_edges: 30 30 | use_cf: False 31 | class_label: [] 32 | 33 | furniture: 34 | surfpos_weight: furniture_ldm_surfpos.pt 35 | surfz_weight: furniture_ldm_surfz.pt 36 | edgepos_weight: furniture_ldm_edgepos.pt 37 | edgez_weight: furniture_ldm_edgez.pt 38 | surfvae_weight: furniture_vae_surf.pt 39 | edgevae_weight: furniture_vae_edge.pt 40 | save_folder: samples_furniture 41 | batch_size: 16 42 | z_threshold: 0.2 43 | bbox_threshold: 0.08 44 | num_surfaces: 60 45 | num_edges: 40 46 | use_cf: True 47 | class_label: chair # option: [bathtub/bed/bench/bookshelf/cabinet/chair/couch/lamp/sofa/table] 48 | -------------------------------------------------------------------------------- /vae.py: -------------------------------------------------------------------------------- 1 | import os 2 | from utils import get_args_vae 3 | 4 | # Parse input augments 5 | args = get_args_vae() 6 | 7 | # Set PyTorch to use only the specified GPU 8 | os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(map(str, args.gpu)) 9 | 10 | # Make project directory if not exist 11 | if not os.path.exists(args.save_dir): 12 | os.makedirs(args.save_dir) 13 | 14 | from trainer import SurfVAETrainer 15 | from dataset import SurfData 16 | from trainer import EdgeVAETrainer 17 | from dataset import EdgeData 18 | 19 | def run(args): 20 | # Initialize dataset loader and trainer 21 | if args.option == 'surface': 22 | train_dataset = SurfData(args.data, args.train_list, validate=False, aug=args.data_aug) 23 | val_dataset = SurfData(args.data, args.val_list, validate=True, aug=False) 24 | vae = SurfVAETrainer(args, train_dataset, val_dataset) 25 | else: 26 | assert args.option == 'edge', 'please choose between surface or edge' 27 | train_dataset = EdgeData(args.data, args.train_list, validate=False, aug=args.data_aug) 28 | val_dataset = EdgeData(args.data, args.val_list, validate=True, aug=False) 29 | vae = EdgeVAETrainer(args, train_dataset, val_dataset) 30 | 31 | # Main training loop 32 | print('Start training...') 33 | 34 | for _ in range(args.train_nepoch): 35 | 36 | # Train for one epoch 37 | vae.train_one_epoch() 38 | 39 | # Evaluate model performance on validation set 40 | if vae.epoch % args.test_nepoch == 0: 41 | vae.test_val() 42 | 43 | # save model 44 | if vae.epoch % args.save_nepoch == 0: 45 | vae.save_model() 46 | return 47 | 48 | 49 | if __name__ == "__main__": 50 | run(args) -------------------------------------------------------------------------------- /train_vae.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash\ 2 | 3 | 4 | ### DeepCAD VAE Training ### 5 | python vae.py --data data_process/deepcad_parsed \ 6 | --train_list data_process/deepcad_data_split_6bit_surface.pkl \ 7 | --val_list data_process/deepcad_data_split_6bit.pkl \ 8 | --option surface --gpu 0 --env deepcad_vae_surf --train_nepoch 400 --data_aug 9 | 10 | python vae.py --data data_process/deepcad_parsed \ 11 | --train_list data_process/deepcad_data_split_6bit_edge.pkl \ 12 | --val_list data_process/deepcad_data_split_6bit.pkl \ 13 | --option edge --gpu 0 --env deepcad_vae_edge --train_nepoch 400 --data_aug 14 | 15 | 16 | ### ABC VAE Training ### 17 | python vae.py --data data_process/abc_parsed \ 18 | --train_list data_process/abc_data_split_6bit_surface.pkl \ 19 | --val_list data_process/abc_data_split_6bit.pkl \ 20 | --option surface --gpu 0 --env abc_vae_surf --train_nepoch 200 --data_aug 21 | 22 | python vae.py --data data_process/abc_parsed \ 23 | --train_list data_process/abc_data_split_6bit_edge.pkl \ 24 | --val_list data_process/abc_data_split_6bit.pkl \ 25 | --option edge --gpu 0 --env abc_vae_edge --train_nepoch 200 --data_aug 26 | 27 | 28 | ### Furniture VAE Training (fintune) ### 29 | python vae.py --data data_process/furniture_parsed \ 30 | --train_list data_process/furniture_data_split_6bit_surface.pkl \ 31 | --val_list data_process/furniture_data_split_6bit.pkl \ 32 | --option surface --gpu 0 --env furniture_vae_surf --train_nepoch 200 --finetune \ 33 | --weight proj_log/deepcad_vae_surf.pt 34 | 35 | python vae.py --data data_process/furniture_parsed \ 36 | --train_list data_process/furniture_data_split_6bit_edge.pkl \ 37 | --val_list data_process/furniture_data_split_6bit.pkl \ 38 | --option edge --gpu 0 --env furniture_vae_edge --train_nepoch 200 --finetune \ 39 | --weight proj_log/deepcad_vae_edge.pt -------------------------------------------------------------------------------- /data_process/deduplicate_surfedge.py: -------------------------------------------------------------------------------- 1 | import math 2 | import pickle 3 | import argparse 4 | from tqdm import tqdm 5 | from hashlib import sha256 6 | from convert_utils import * 7 | 8 | 9 | parser = argparse.ArgumentParser() 10 | parser.add_argument("--data", type=str, help="CAD .pkl file", required=True) 11 | parser.add_argument("--list", type=str, help="UID list", required=True) 12 | parser.add_argument("--edge", action='store_true', help='Process edge instead of surface') 13 | parser.add_argument("--bit", type=int, help='Deduplicate precision') 14 | parser.add_argument("--option", type=str, choices=['abc', 'deepcad', 'furniture'], default='abc', 15 | help="Choose between dataset option [abc/deepcad/furniture] (default: abc)") 16 | args = parser.parse_args() 17 | 18 | 19 | with open(args.list, "rb") as file: 20 | data_list = pickle.load(file)['train'] 21 | 22 | unique_data = [] 23 | unique_hash = set() 24 | total = 0 25 | 26 | for path_idx, uid in tqdm(enumerate(data_list)): 27 | if args.option == 'furniture': 28 | path = os.path.join(args.data, uid) 29 | else: 30 | path = os.path.join(args.data, str(math.floor(int(uid.split('.')[0])/10000)).zfill(4), uid) 31 | with open(path, "rb") as file: 32 | data = pickle.load(file) 33 | 34 | _, _, surf_ncs, edge_ncs, _, _, _, _, _, _, _, _ = data.values() 35 | 36 | if args.edge: 37 | data = edge_ncs 38 | else: 39 | data = surf_ncs 40 | 41 | data_bits = real2bit(data, n_bits=args.bit) 42 | 43 | for np_bit, np_real in zip(data_bits, data): 44 | total += 1 45 | 46 | # Reshape the array to a 2D array 47 | np_bit = np_bit.reshape(-1, 3) 48 | data_hash = sha256(np_bit.tobytes()).hexdigest() 49 | 50 | prev_len = len(unique_hash) 51 | unique_hash.add(data_hash) 52 | 53 | if prev_len < len(unique_hash): 54 | unique_data.append(np_real) 55 | else: 56 | continue 57 | 58 | if path_idx % 2000 == 0: 59 | print(len(unique_hash)/total) 60 | 61 | 62 | if args.edge: 63 | save_path = args.list.split('.')[0] + '_edge.pkl' 64 | else: 65 | save_path = args.list.split('.')[0] + '_surface.pkl' 66 | 67 | with open(save_path, "wb") as tf: 68 | pickle.dump(unique_data, tf) 69 | -------------------------------------------------------------------------------- /ldm.py: -------------------------------------------------------------------------------- 1 | import os 2 | from utils import * 3 | 4 | # Parse input augments 5 | args = get_args_ldm() 6 | 7 | # Set PyTorch to use only the specified GPU 8 | os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(map(str, args.gpu)) 9 | 10 | # Make project directory if not exist 11 | if not os.path.exists(args.save_dir): 12 | os.makedirs(args.save_dir) 13 | 14 | from dataset import * 15 | from trainer import * 16 | 17 | def run(args): 18 | # Initialize dataset and trainer 19 | if args.option == 'surfpos': 20 | train_dataset = SurfPosData(args.data, args.list, validate=False, aug=args.data_aug, args=args) 21 | val_dataset = SurfPosData(args.data, args.list, validate=True, aug=False, args=args) 22 | ldm = SurfPosTrainer(args, train_dataset, val_dataset) 23 | 24 | elif args.option == 'surfz': 25 | train_dataset = SurfZData(args.data, args.list, validate=False, aug=args.data_aug, args=args) 26 | val_dataset = SurfZData(args.data, args.list, validate=True, aug=False, args=args) 27 | ldm = SurfZTrainer(args, train_dataset, val_dataset) 28 | 29 | elif args.option == 'edgepos': 30 | train_dataset = EdgePosData(args.data, args.list, validate=False, aug=args.data_aug, args=args) 31 | val_dataset = EdgePosData(args.data, args.list, validate=True, aug=False, args=args) 32 | ldm = EdgePosTrainer(args, train_dataset, val_dataset) 33 | 34 | elif args.option == 'edgez': 35 | train_dataset = EdgeZData(args.data, args.list, validate=False, aug=args.data_aug, args=args) 36 | val_dataset = EdgeZData(args.data, args.list, validate=True, aug=False, args=args) 37 | ldm = EdgeZTrainer(args, train_dataset, val_dataset) 38 | 39 | else: 40 | assert False, 'please choose between [surfpos, surfz, edgepos, edgez]' 41 | 42 | print('Start training...') 43 | 44 | # Main training loop 45 | for _ in range(args.train_nepoch): 46 | 47 | # Train for one epoch 48 | ldm.train_one_epoch() 49 | 50 | # Evaluate model performance on validation set 51 | if ldm.epoch % args.test_nepoch == 0: 52 | ldm.test_val() 53 | 54 | # save model 55 | if ldm.epoch % args.save_nepoch == 0: 56 | ldm.save_model() 57 | 58 | return 59 | 60 | 61 | if __name__ == "__main__": 62 | run(args) -------------------------------------------------------------------------------- /data_process/deduplicate_cad.py: -------------------------------------------------------------------------------- 1 | import math 2 | import pickle 3 | import argparse 4 | from tqdm import tqdm 5 | from hashlib import sha256 6 | from convert_utils import * 7 | 8 | parser = argparse.ArgumentParser() 9 | parser.add_argument("--data", type=str, help="Data folder path", required=True) 10 | parser.add_argument("--bit", type=int, help='Deduplicate precision') 11 | parser.add_argument("--option", type=str, choices=['abc', 'deepcad', 'furniture'], default='abc', 12 | help="Choose between dataset option [abc/deepcad/furniture] (default: abc)") 13 | args = parser.parse_args() 14 | 15 | if args.option == 'deepcad': 16 | OUTPUT = f'deepcad_data_split_{args.bit}bit.pkl' 17 | elif args.option == 'abc': 18 | OUTPUT = f'abc_data_split_{args.bit}bit.pkl' 19 | else: 20 | OUTPUT = f'furniture_data_split_{args.bit}bit.pkl' 21 | 22 | # Load all STEP folders 23 | if args.option == 'furniture': 24 | train, val_path, test_path = load_furniture_pkl(args.data) 25 | else: 26 | train, val_path, test_path = load_abc_pkl(args.data, args.option=='deepcad') 27 | 28 | # Remove duplicate for the training set 29 | train_path = [] 30 | unique_hash = set() 31 | total = 0 32 | 33 | for path_idx, uid in tqdm(enumerate(train)): 34 | total += 1 35 | 36 | # Load pkl data 37 | if args.option == 'furniture': 38 | path = os.path.join(args.data, uid) 39 | else: 40 | path = os.path.join(args.data, str(math.floor(int(uid.split('.')[0])/10000)).zfill(4), uid) 41 | with open(path, "rb") as file: 42 | data = pickle.load(file) 43 | 44 | # Hash the surface sampled points 45 | surfs_wcs = data['surf_wcs'] 46 | surf_hash_total = [] 47 | for surf in surfs_wcs: 48 | np_bit = real2bit(surf, n_bits=args.bit).reshape(-1, 3) # bits 49 | data_hash = sha256(np_bit.tobytes()).hexdigest() 50 | surf_hash_total.append(data_hash) 51 | surf_hash_total = sorted(surf_hash_total) 52 | data_hash = '_'.join(surf_hash_total) 53 | 54 | # Save non-duplicate shapes 55 | prev_len = len(unique_hash) 56 | unique_hash.add(data_hash) 57 | if prev_len < len(unique_hash): 58 | train_path.append(uid) 59 | else: 60 | continue 61 | 62 | if path_idx % 2000 == 0: 63 | print(len(unique_hash)/total) 64 | 65 | # save data 66 | data_path = { 67 | 'train':train_path, 68 | 'val':val_path, 69 | 'test':test_path, 70 | } 71 | with open(OUTPUT, "wb") as tf: 72 | pickle.dump(data_path, tf) 73 | 74 | -------------------------------------------------------------------------------- /sample_points.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | from tqdm import tqdm 4 | import multiprocessing 5 | from pathlib import Path 6 | import trimesh 7 | from trimesh.sample import sample_surface 8 | from plyfile import PlyData, PlyElement 9 | import numpy as np 10 | 11 | def write_ply(points, filename, text=False): 12 | """ input: Nx3, write points to filename as PLY format. """ 13 | points = [(points[i,0], points[i,1], points[i,2]) for i in range(points.shape[0])] 14 | vertex = np.array(points, dtype=[('x', 'f4'), ('y', 'f4'),('z', 'f4')]) 15 | el = PlyElement.describe(vertex, 'vertex', comments=['vertices']) 16 | with open(filename, mode='wb') as f: 17 | PlyData([el], text=text).write(f) 18 | 19 | 20 | def find_files(folder, extension): 21 | return sorted([Path(os.path.join(folder, f)) for f in os.listdir(folder) if f.endswith(extension)]) 22 | 23 | 24 | def load_data_with_prefix(root_folder, prefix): 25 | data_files = [] 26 | 27 | # Walk through the directory tree starting from the root folder 28 | for root, dirs, files in os.walk(root_folder): 29 | for filename in files: 30 | # Check if the file ends with the specified prefix 31 | if filename.endswith(prefix): 32 | file_path = os.path.join(root, filename) 33 | data_files.append(file_path) 34 | 35 | return data_files 36 | 37 | class SamplePoints: 38 | """ 39 | Perform sampleing of points. 40 | """ 41 | 42 | def __init__(self): 43 | """ 44 | Constructor. 45 | """ 46 | parser = self.get_parser() 47 | self.options = parser.parse_args() 48 | 49 | 50 | def get_parser(self): 51 | """ 52 | Get parser of tool. 53 | 54 | :return: parser 55 | """ 56 | parser = argparse.ArgumentParser(description='Scale a set of meshes stored as OFF files.') 57 | parser.add_argument('--in_dir', type=str, help='Path to input directory.') 58 | parser.add_argument('--out_dir', type=str, help='Path to output directory; files within are overwritten!') 59 | return parser 60 | 61 | 62 | def run_parallel(self, path): 63 | fileName = os.path.join(self.options.out_dir, path.split('/')[-1][:-4]) 64 | 65 | N_POINTS = 2000 66 | out_mesh = trimesh.load(path) 67 | out_pc, _ = sample_surface(out_mesh, N_POINTS) 68 | save_path = os.path.join(fileName+'.ply') 69 | write_ply(out_pc, save_path) 70 | return 71 | 72 | 73 | def run(self): 74 | """ 75 | Run simplification. 76 | """ 77 | if not os.path.exists(self.options.out_dir): 78 | os.makedirs(self.options.out_dir) 79 | 80 | shape_paths = load_data_with_prefix(self.options.in_dir, '.stl') #+ load_data_with_prefix(self.options.in_dir, '.obj') 81 | # for path in shape_paths: 82 | # self.run_parallel(path) 83 | num_cpus = multiprocessing.cpu_count() 84 | convert_iter = multiprocessing.Pool(num_cpus).imap(self.run_parallel, shape_paths) 85 | for _ in tqdm(convert_iter, total=len(shape_paths)): 86 | pass 87 | 88 | 89 | if __name__ == '__main__': 90 | app = SamplePoints() 91 | app.run() 92 | -------------------------------------------------------------------------------- /train_ldm.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash\ 2 | 3 | ### Train the Latent Diffusion Model ### 4 | # --data_aug is optional 5 | # max_face 30, max_edge 20 for deepcad 6 | # max_face 50, max_edge 30 for abc/furniture 7 | # --surfvae refer to the surface vae weights 8 | # --edgevae refer to the edge vae weights 9 | 10 | ### Training DeepCAD Latent Diffusion Model ### 11 | python ldm.py --data data_process/deepcad_parsed \ 12 | --list data_process/deepcad_data_split_6bit.pkl --option surfpos --gpu 0 1 \ 13 | --env deepcad_ldm_surfpos --train_nepoch 3000 --test_nepoch 200 --save_nepoch 200 \ 14 | --max_face 30 --max_edge 20 15 | 16 | python ldm.py --data data_process/deepcad_parsed \ 17 | --list data_process/deepcad_data_split_6bit.pkl --option surfz \ 18 | --surfvae proj_log/deepcad_vae_surf.pt --gpu 0 1 \ 19 | --env deepcad_ldm_surfz --train_nepoch 3000 --batch_size 256 \ 20 | --max_face 30 --max_edge 20 21 | 22 | python ldm.py --data data_process/deepcad_parsed \ 23 | --list data_process/deepcad_data_split_6bit.pkl --option edgepos \ 24 | --surfvae proj_log/deepcad_vae_surf.pt --gpu 0 1 \ 25 | --env deepcad_ldm_edgepos --train_nepoch 1000 --batch_size 128 \ 26 | --max_face 30 --max_edge 20 27 | 28 | python ldm.py --data data_process/deepcad_parsed \ 29 | --list data_process/deepcad_data_split_6bit.pkl --option edgez \ 30 | --surfvae proj_log/deepcad_vae_surf.pt --edgevae proj_log/deepcad_vae_edge.pt --gpu 0 1 \ 31 | --env deepcad_ldm_edgez --train_nepoch 1000 --batch_size 128 \ 32 | --max_face 30 --max_edge 20 33 | 34 | 35 | ### Training ABC Latent Diffusion Model ### 36 | python ldm.py --data data_process/abc_parsed \ 37 | --list data_process/abc_data_split_6bit.pkl --option surfpos --gpu 0 1 \ 38 | --env abc_ldm_surfpos --train_nepoch 1000 --test_nepoch 200 --save_nepoch 200 \ 39 | --max_face 50 --max_edge 30 40 | 41 | python ldm.py --data data_process/abc_parsed \ 42 | --list data_process/abc_data_split_6bit.pkl --option surfz \ 43 | --surfvae proj_log/abc_vae_surf.pt --gpu 0 1 \ 44 | --env abc_ldm_surfz --train_nepoch 1000 --batch_size 256 \ 45 | --max_face 50 --max_edge 30 46 | 47 | python ldm.py --data data_process/abc_parsed \ 48 | --list data_process/abc_data_split_6bit.pkl --option edgepos \ 49 | --surfvae proj_log/abc_vae_surf.pt --gpu 0 1 \ 50 | --env abc_ldm_edgepos --train_nepoch 300 --batch_size 64 \ 51 | --max_face 50 --max_edge 30 52 | 53 | python ldm.py --data data_process/abc_parsed \ 54 | --list data_process/abc_data_split_6bit.pkl --option edgez \ 55 | --surfvae proj_log/abc_vae_surf.pt --edgevae proj_log/abc_vae_edge.pt --gpu 0 1 \ 56 | --env abc_ldm_edgez --train_nepoch 300 --batch_size 64 \ 57 | --max_face 50 --max_edge 30 58 | 59 | 60 | ### Training Furniture Latent Diffusion Model (classifier-free) ### 61 | python ldm.py --data data_process/furniture_parsed \ 62 | --list data_process/furniture_data_split_6bit.pkl --option surfpos --gpu 0 1 \ 63 | --env furniture_ldm_surfpos --train_nepoch 3000 --test_nepoch 200 --save_nepoch 200 \ 64 | --max_face 50 --max_edge 30 --cf 65 | 66 | python ldm.py --data data_process/furniture_parsed \ 67 | --list data_process/furniture_data_split_6bit.pkl --option surfz \ 68 | --surfvae proj_log/furniture_vae_surf.pt --gpu 0 1 \ 69 | --env furniture_ldm_surfz --train_nepoch 3000 --batch_size 256 \ 70 | --max_face 50 --max_edge 30 --cf 71 | 72 | python ldm.py --data data_process/furniture_parsed \ 73 | --list data_process/furniture_data_split_6bit.pkl --option edgepos \ 74 | --surfvae proj_log/furniture_vae_surf.pt --gpu 0 1 \ 75 | --env furniture_ldm_edgepos --train_nepoch 1000 --batch_size 64 \ 76 | --max_face 50 --max_edge 30 --cf 77 | 78 | python ldm.py --data data_process/furniture_parsed \ 79 | --list data_process/furniture_data_split_6bit.pkl --option edgez \ 80 | --surfvae proj_log/furniture_vae_surf.pt --edgevae proj_log/furniture_vae_edge.pt --gpu 0 1 \ 81 | --env furniture_ldm_edgez --train_nepoch 1000 --batch_size 64 \ 82 | --max_face 50 --max_edge 30 --cf -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # From: https://raw.githubusercontent.com/github/gitignore/main/Python.gitignore 2 | # Byte-compiled / optimized / DLL files 3 | __pycache__/ 4 | *.py[cod] 5 | *$py.class 6 | 7 | # C extensions 8 | *.so 9 | 10 | # Distribution / packaging 11 | .Python 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | cover/ 54 | 55 | # 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Environments 126 | .env 127 | .venv 128 | env/ 129 | venv/ 130 | ENV/ 131 | env.bak/ 132 | venv.bak/ 133 | 134 | # Spyder project settings 135 | .spyderproject 136 | .spyproject 137 | 138 | # Rope project settings 139 | .ropeproject 140 | 141 | # mkdocs documentation 142 | /site 143 | 144 | # mypy 145 | .mypy_cache/ 146 | .dmypy.json 147 | dmypy.json 148 | 149 | # Pyre type checker 150 | .pyre/ 151 | 152 | # pytype static type analyzer 153 | .pytype/ 154 | 155 | # Cython debug symbols 156 | cython_debug/ 157 | 158 | # PyCharm 159 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 160 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 161 | # and can be added to the global gitignore or merged into this file. For a more nuclear 162 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 163 | .idea/ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry (SIGGRAPH 2024) 2 | 3 | [![arXiv](https://img.shields.io/badge/📃-arXiv%20-red.svg)](https://arxiv.org/abs/2401.15563) 4 | [![webpage](https://img.shields.io/badge/🌐-Website%20-blue.svg)](https://brepgen.github.io) 5 | [![Youtube](https://img.shields.io/badge/📽️-Video%20-orchid.svg)](https://www.youtube.com/xxx) 6 | 7 | *[Xiang Xu](https://samxuxiang.github.io/), [Joseph Lambourne](https://www.research.autodesk.com/people/joseph-george-lambourne/), 8 | [Pradeep Jayaraman](https://www.research.autodesk.com/people/pradeep-kumar-jayaraman/), [Zhengqing Wang](https://www.linkedin.com/in/zhengqing-wang-485854241/?originalSubdomain=ca), [Karl Willis](https://www.karlddwillis.com/), and [Yasutaka Furukawa](https://yasu-furukawa.github.io/)* 9 | 10 | ![alt BrepGen](resources/teaser.jpg) 11 | 12 | > We present a diffusion-based generative approach that directly outputs a CAD B-rep. BrepGen uses a novel structured latent geometry to encode the CAD geometry and topology. A top-down generation approach is used to denoise the faces, edges, and vertices. 13 | 14 | 15 | ## Requirements 16 | 17 | ### Environment (Tested) 18 | - Linux 19 | - Python 3.9 20 | - CUDA 11.8 21 | - PyTorch 2.2 22 | - Diffusers 0.27 23 | 24 | 25 | ### Dependencies 26 | 27 | Install PyTorch and other dependencies: 28 | ``` 29 | conda create --name brepgen_env python=3.9 -y 30 | conda activate brepgen_env 31 | 32 | pip install -r requirements.txt 33 | pip install chamferdist 34 | ``` 35 | 36 | If `chamferdist` fails to install here are a few options to try: 37 | 38 | - If there is a CUDA version mismatch error, then try setting the `CUDA_HOME` environment variable to point to CUDA installation folder. The CUDA version of this folder must match with PyTorch's version i.e. 11.8. 39 | 40 | - Try [building from source](https://github.com/krrish94/chamferdist?tab=readme-ov-file#building-from-source). 41 | 42 | Install OCCWL following the instruction [here](https://github.com/AutodeskAILab/occwl). 43 | If conda is stuck in "Solving environment..." there are two options to try: 44 | 45 | - Try using `mamba` as suggested in occwl's README. 46 | 47 | - Install pythonOCC: https://github.com/tpaviot/pythonocc-core?tab=readme-ov-file#install-with-conda and occwl manually: `pip install git+https://github.com/AutodeskAILab/occwl`. 48 | 49 | ## Data 50 | Download [ABC](https://archive.nyu.edu/handle/2451/43778) STEP files (100 folders). 51 | 52 | Download [Furniture Data](https://drive.google.com/drive/folders/1WpV_rgJDXEkBoWaQsqEoO9Ir8CABI8oP?usp=sharing). JSON file contains object UIDs that refer to the original STEP files. 53 | 54 | The faces, edges, and vertices need to be extracted from the STEP files. 55 | 56 | Process the B-reps (under ```data_process``` folder): 57 | 58 | sh process.sh 59 | 60 | 61 | Remove repeated CAD models (under ```data_process``` folder, default is ```6 bit``` ): 62 | 63 | sh deduplicate.sh 64 | 65 | You can download the deduplicated files for [DeepCAD](https://drive.google.com/drive/folders/1N_60VCZKYgPviQgP8lwCOVXrzu9Midfe?usp=drive_link), and [ABC](https://drive.google.com/drive/folders/1bA90Rz5EcwaUhUrgFbSIpgdJ0aeDjy3v?usp=drive_link). 66 | 67 | 68 | 69 | ## Training 70 | Train the surface and edge VAE (wandb for logging): 71 | 72 | sh train_vae.sh 73 | 74 | Train the latent diffusion model (change path to previously trained VAEs): 75 | 76 | sh train_ldm.sh 77 | 78 | ```--cf``` classifier-free training for the Furniture dataset. 79 | 80 | ```--data_aug``` randomly rotate the CAD model during training (optional). 81 | 82 | 83 | 84 | 85 | ## Generation and Evaluation 86 | Randomly generate B-reps from Gaussian noise, both STEP and STL files will be saved: 87 | 88 | python sample.py --mode abc 89 | 90 | This will load the settings in ```eval_config.yaml```. Make sure to update model paths to the correct folder. 91 | 92 | Run this script for evaluation (change the path to generated data folder, with at least 3,000 samples): 93 | 94 | sh eval.sh 95 | 96 | This computes the JSD, MMD, and COV scores. Please also download sampled point clouds for [test set](https://drive.google.com/drive/folders/1kqxSDkS2gUN9_qpuWotFDhl4t7czbfOc?usp=sharing). 97 | 98 | 99 | ## Pretrained Checkpoint 100 | We also provide the individual checkpoints trained on different datasets. 101 | | **Source Dataset** | | | 102 | |--------------------|-----------| -----------| 103 | | DeepCAD | [vae model](https://drive.google.com/drive/folders/1UZYqJ2EmTjzeTcNr_NL3bPpU4WrufvQa?usp=drive_link) | [latent diffusion model](https://drive.google.com/drive/folders/1jonuCzoTBFOKKlnaoGlbmhT6YlnH0lma?usp=drive_link) | 104 | | ABC | [vae model](https://drive.google.com/drive/folders/18Ib9L0kpFf4ylZIRTCYFhXZB_GVIUm53?usp=drive_link) | [latent diffusion model](https://drive.google.com/drive/folders/1hv7ZUcU-L3J0LiONK60-TEh7sAN0zfve?usp=drive_link) | 105 | 106 | 107 | ## Acknowledgement 108 | This research is partially supported by NSERC Discovery Grants with Accelerator Supplements and DND/NSERC Discovery Grant 109 | Supplement, NSERC Alliance Grants, and John R. Evans Leaders Fund (JELF). We also thank Onshape for their support and access of 110 | the publicly available CAD models. 111 | 112 | 113 | ## Citation 114 | If you find our work useful in your research, please cite the following paper 115 | ``` 116 | @article{xu2024brepgen, 117 | title={BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry}, 118 | author={Xu, Xiang and Lambourne, Joseph G and Jayaraman, Pradeep Kumar and Wang, Zhengqing and Willis, Karl DD and Furukawa, Yasutaka}, 119 | journal={arXiv preprint arXiv:2401.15563}, 120 | year={2024} 121 | } 122 | ``` 123 | -------------------------------------------------------------------------------- /data_process/process_brep.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pickle 3 | import argparse 4 | from tqdm import tqdm 5 | from multiprocessing.pool import Pool 6 | from convert_utils import * 7 | from occwl.io import load_step 8 | 9 | 10 | # To speed up processing, define maximum threshold 11 | MAX_FACE = 70 12 | 13 | def normalize(surf_pnts, edge_pnts, corner_pnts): 14 | """ 15 | Various levels of normalization 16 | """ 17 | # Global normalization to -1~1 18 | total_points = np.array(surf_pnts).reshape(-1, 3) 19 | min_vals = np.min(total_points, axis=0) 20 | max_vals = np.max(total_points, axis=0) 21 | global_offset = min_vals + (max_vals - min_vals)/2 22 | global_scale = max(max_vals - min_vals) 23 | assert global_scale != 0, 'scale is zero' 24 | 25 | surfs_wcs, edges_wcs, surfs_ncs, edges_ncs = [],[],[],[] 26 | 27 | # Normalize corner 28 | corner_wcs = (corner_pnts - global_offset[np.newaxis,:]) / (global_scale * 0.5) 29 | 30 | # Normalize surface 31 | for surf_pnt in surf_pnts: 32 | # Normalize CAD to WCS 33 | surf_pnt_wcs = (surf_pnt - global_offset[np.newaxis,np.newaxis,:]) / (global_scale * 0.5) 34 | surfs_wcs.append(surf_pnt_wcs) 35 | # Normalize Surface to NCS 36 | min_vals = np.min(surf_pnt_wcs.reshape(-1,3), axis=0) 37 | max_vals = np.max(surf_pnt_wcs.reshape(-1,3), axis=0) 38 | local_offset = min_vals + (max_vals - min_vals)/2 39 | local_scale = max(max_vals - min_vals) 40 | pnt_ncs = (surf_pnt_wcs - local_offset[np.newaxis,np.newaxis,:]) / (local_scale * 0.5) 41 | surfs_ncs.append(pnt_ncs) 42 | 43 | # Normalize edge 44 | for edge_pnt in edge_pnts: 45 | # Normalize CAD to WCS 46 | edge_pnt_wcs = (edge_pnt - global_offset[np.newaxis,:]) / (global_scale * 0.5) 47 | edges_wcs.append(edge_pnt_wcs) 48 | # Normalize Edge to NCS 49 | min_vals = np.min(edge_pnt_wcs.reshape(-1,3), axis=0) 50 | max_vals = np.max(edge_pnt_wcs.reshape(-1,3), axis=0) 51 | local_offset = min_vals + (max_vals - min_vals)/2 52 | local_scale = max(max_vals - min_vals) 53 | pnt_ncs = (edge_pnt_wcs - local_offset) / (local_scale * 0.5) 54 | edges_ncs.append(pnt_ncs) 55 | assert local_scale != 0, 'scale is zero' 56 | 57 | surfs_wcs = np.stack(surfs_wcs) 58 | surfs_ncs = np.stack(surfs_ncs) 59 | edges_wcs = np.stack(edges_wcs) 60 | edges_ncs = np.stack(edges_ncs) 61 | 62 | return surfs_wcs, edges_wcs, surfs_ncs, edges_ncs, corner_wcs 63 | 64 | 65 | def parse_solid(solid): 66 | """ 67 | Parse the surface, curve, face, edge, vertex in a CAD solid. 68 | 69 | Args: 70 | - solid (occwl.solid): A single brep solid in occwl data format. 71 | 72 | Returns: 73 | - data: A dictionary containing all parsed data 74 | """ 75 | assert isinstance(solid, Solid) 76 | 77 | # Split closed surface and closed curve to halve 78 | solid = solid.split_all_closed_faces(num_splits=0) 79 | solid = solid.split_all_closed_edges(num_splits=0) 80 | 81 | if len(list(solid.faces())) > MAX_FACE: 82 | return None 83 | 84 | # Extract all B-rep primitives and their adjacency information 85 | face_pnts, edge_pnts, edge_corner_pnts, edgeFace_IncM, faceEdge_IncM = extract_primitive(solid) 86 | 87 | # Normalize the CAD model 88 | surfs_wcs, edges_wcs, surfs_ncs, edges_ncs, corner_wcs = normalize(face_pnts, edge_pnts, edge_corner_pnts) 89 | 90 | # Remove duplicate and merge corners 91 | corner_wcs = np.round(corner_wcs,4) 92 | corner_unique = [] 93 | for corner_pnt in corner_wcs.reshape(-1,3): 94 | if len(corner_unique) == 0: 95 | corner_unique = corner_pnt.reshape(1,3) 96 | else: 97 | # Check if it exist or not 98 | exists = np.any(np.all(corner_unique == corner_pnt, axis=1)) 99 | if exists: 100 | continue 101 | else: 102 | corner_unique = np.concatenate([corner_unique, corner_pnt.reshape(1,3)], 0) 103 | 104 | # Edge-corner adjacency 105 | edgeCorner_IncM = [] 106 | for edge_corner in corner_wcs: 107 | start_corner_idx = np.where((corner_unique == edge_corner[0]).all(axis=1))[0].item() 108 | end_corner_idx = np.where((corner_unique == edge_corner[1]).all(axis=1))[0].item() 109 | edgeCorner_IncM.append([start_corner_idx, end_corner_idx]) 110 | edgeCorner_IncM = np.array(edgeCorner_IncM) 111 | 112 | # Surface global bbox 113 | surf_bboxes = [] 114 | for pnts in surfs_wcs: 115 | min_point, max_point = get_bbox(pnts.reshape(-1,3)) 116 | surf_bboxes.append( np.concatenate([min_point, max_point])) 117 | surf_bboxes = np.vstack(surf_bboxes) 118 | 119 | # Edge global bbox 120 | edge_bboxes = [] 121 | for pnts in edges_wcs: 122 | min_point, max_point = get_bbox(pnts.reshape(-1,3)) 123 | edge_bboxes.append(np.concatenate([min_point, max_point])) 124 | edge_bboxes = np.vstack(edge_bboxes) 125 | 126 | # Convert to float32 to save space 127 | data = { 128 | 'surf_wcs':surfs_wcs.astype(np.float32), 129 | 'edge_wcs':edges_wcs.astype(np.float32), 130 | 'surf_ncs':surfs_ncs.astype(np.float32), 131 | 'edge_ncs':edges_ncs.astype(np.float32), 132 | 'corner_wcs':corner_wcs.astype(np.float32), 133 | 'edgeFace_adj': edgeFace_IncM, 134 | 'edgeCorner_adj':edgeCorner_IncM, 135 | 'faceEdge_adj':faceEdge_IncM, 136 | 'surf_bbox_wcs':surf_bboxes.astype(np.float32), 137 | 'edge_bbox_wcs':edge_bboxes.astype(np.float32), 138 | 'corner_unique':corner_unique.astype(np.float32), 139 | } 140 | 141 | return data 142 | 143 | 144 | def process(step_folder): 145 | """ 146 | Helper function to load step files and process in parallel 147 | 148 | Args: 149 | - step_folder (str): Path to the STEP parent folder. 150 | 151 | Returns: 152 | - Complete status: Valid (1) / Non-valid (0). 153 | """ 154 | try: 155 | # Load cad data 156 | if step_folder.endswith('.step'): 157 | step_path = step_folder 158 | process_furniture = True 159 | else: 160 | for _, _, files in os.walk(step_folder): 161 | assert len(files) == 1 162 | step_path = os.path.join(step_folder, files[0]) 163 | process_furniture = False 164 | 165 | # Check single solid 166 | cad_solid = load_step(step_path) 167 | if len(cad_solid)!=1: 168 | print('Skipping multi solids...') 169 | return 0 170 | 171 | # Start data parsing 172 | data = parse_solid(cad_solid[0]) 173 | if data is None: 174 | print ('Exceeding threshold...') 175 | return 0 # number of faces or edges exceed pre-determined threshold 176 | 177 | # Save the parsed result 178 | if process_furniture: 179 | data_uid = step_path.split('/')[-2] + '_' + step_path.split('/')[-1] 180 | sub_folder = step_path.split('/')[-3] 181 | else: 182 | data_uid = step_path.split('/')[-2] 183 | sub_folder = data_uid[:4] 184 | 185 | if data_uid.endswith('.step'): 186 | data_uid = data_uid[:-5] # furniture avoid .step 187 | 188 | data['uid'] = data_uid 189 | save_folder = os.path.join(OUTPUT, sub_folder) 190 | if not os.path.exists(save_folder): 191 | os.makedirs(save_folder) 192 | 193 | save_path = os.path.join(save_folder, data['uid']+'.pkl') 194 | with open(save_path, "wb") as tf: 195 | pickle.dump(data, tf) 196 | 197 | return 1 198 | 199 | except Exception as e: 200 | print('not saving due to error...') 201 | return 0 202 | 203 | 204 | if __name__ == '__main__': 205 | parser = argparse.ArgumentParser() 206 | parser.add_argument("--input", type=str, help="Data folder path", required=True) 207 | parser.add_argument("--option", type=str, choices=['abc', 'deepcad', 'furniture'], default='abc', 208 | help="Choose between dataset option [abc/deepcad/furniture] (default: abc)") 209 | parser.add_argument("--interval", type=int, help="Data range index, only required for abc/deepcad") 210 | args = parser.parse_args() 211 | 212 | if args.option == 'deepcad': 213 | OUTPUT = 'deepcad_parsed' 214 | elif args.option == 'abc': 215 | OUTPUT = 'abc_parsed' 216 | else: 217 | OUTPUT = 'furniture_parsed' 218 | 219 | # Load all STEP files 220 | if args.option == 'furniture': 221 | step_dirs = load_furniture_step(args.input) 222 | else: 223 | step_dirs = load_abc_step(args.input, args.option=='deepcad') 224 | step_dirs = step_dirs[args.interval*10000 : (args.interval+1)*10000] 225 | 226 | # Process B-reps in parallel 227 | valid = 0 228 | convert_iter = Pool(os.cpu_count()).imap(process, step_dirs) 229 | for status in tqdm(convert_iter, total=len(step_dirs)): 230 | valid += status 231 | print(f'Done... Data Converted Ratio {100.0*valid/len(step_dirs)}%') 232 | 233 | 234 | -------------------------------------------------------------------------------- /data_process/convert_utils.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import random 4 | import numpy as np 5 | from occwl.uvgrid import ugrid, uvgrid 6 | from occwl.compound import Compound 7 | from occwl.solid import Solid 8 | from occwl.shell import Shell 9 | from occwl.entity_mapper import EntityMapper 10 | 11 | 12 | def get_bbox(point_cloud): 13 | """ 14 | Get the tighest fitting 3D bounding box giving a set of points (axis-aligned) 15 | """ 16 | # Find the minimum and maximum coordinates along each axis 17 | min_x = np.min(point_cloud[:, 0]) 18 | max_x = np.max(point_cloud[:, 0]) 19 | 20 | min_y = np.min(point_cloud[:, 1]) 21 | max_y = np.max(point_cloud[:, 1]) 22 | 23 | min_z = np.min(point_cloud[:, 2]) 24 | max_z = np.max(point_cloud[:, 2]) 25 | 26 | # Create the 3D bounding box using the min and max values 27 | min_point = np.array([min_x, min_y, min_z]) 28 | max_point = np.array([max_x, max_y, max_z]) 29 | return min_point, max_point 30 | 31 | 32 | def real2bit(data, n_bits=8, min_range=-1, max_range=1): 33 | """Convert vertices in [-1., 1.] to discrete values in [0, n_bits**2 - 1].""" 34 | range_quantize = 2**n_bits - 1 35 | data_quantize = (data - min_range) * range_quantize / (max_range - min_range) 36 | data_quantize = np.clip(data_quantize, a_min=0, a_max=range_quantize) # clip values 37 | return data_quantize.astype(int) 38 | 39 | 40 | def load_abc_pkl(root_dir, use_deepcad): 41 | """ 42 | Recursively searches through a given parent directory and its subdirectories 43 | to find the paths of all ABC .pkl files. 44 | 45 | Args: 46 | - root_dir (str): Path to the root directory where the search begins. 47 | - use_deepcad (bool): Process deepcad or not 48 | 49 | Returns: 50 | - train [str]: A list containing the paths to all .pkl train data 51 | - val [str]: A list containing the paths to all .pkl validation data 52 | - test [str]: A list containing the paths to all .pkl test data 53 | """ 54 | # Load DeepCAD UID 55 | if use_deepcad: 56 | with open('train_val_test_split.json', 'r') as json_file: 57 | deepcad_data = json.load(json_file) 58 | train_uid = set([uid.split('/')[1] for uid in deepcad_data['train']]) 59 | val_uid = set([uid.split('/')[1] for uid in deepcad_data['validation']]) 60 | test_uid = set([uid.split('/')[1] for uid in deepcad_data['test']]) 61 | 62 | # Load ABC UID 63 | else: 64 | full_uids = [] 65 | dirs = [f'{root_dir}/{str(i).zfill(4)}' for i in range(100)] 66 | for folder in dirs: 67 | files = os.listdir(folder) 68 | full_uids += files 69 | # 90-5-5 random split, same as deepcad 70 | random.shuffle(full_uids) # randomly shuffle data 71 | train_uid = full_uids[0:int(len(full_uids)*0.9)] 72 | val_uid = full_uids[int(len(full_uids)*0.9):int(len(full_uids)*0.95)] 73 | test_uid = full_uids[int(len(full_uids)*0.95):] 74 | train_uid = set([uid.split('.')[0] for uid in train_uid]) 75 | val_uid = set([uid.split('.')[0] for uid in val_uid]) 76 | test_uid = set([uid.split('.')[0] for uid in test_uid]) 77 | 78 | train = [] 79 | val = [] 80 | test = [] 81 | dirs = [f'{root_dir}/{str(i).zfill(4)}' for i in range(100)] 82 | for folder in dirs: 83 | files = os.listdir(folder) 84 | for file in files: 85 | key_id = file.split('.')[0] 86 | if key_id in train_uid: 87 | train.append(file) 88 | elif key_id in val_uid: 89 | val.append(file) 90 | elif key_id in test_uid: 91 | test.append(file) 92 | else: 93 | print('unknown uid...') 94 | assert False 95 | return train, val, test 96 | 97 | 98 | def load_furniture_pkl(root_dir): 99 | """ 100 | Recursively searches through a given parent directory and its subdirectories 101 | to find the paths of all furniture .pkl files. 102 | 103 | Args: 104 | - root_dir (str): Path to the root directory where the search begins. 105 | 106 | Returns: 107 | - train [str]: A list containing the paths to all .pkl train data 108 | - val [str]: A list containing the paths to all .pkl validation data 109 | - test [str]: A list containing the paths to all .pkl test data 110 | """ 111 | full_uids = [] 112 | for root, dirs, files in os.walk(root_dir): 113 | for filename in files: 114 | # Check if the file ends with the specified prefix 115 | if filename.endswith('.pkl'): 116 | file_path = os.path.join(root, filename) 117 | full_uids.append(file_path) 118 | 119 | # 90-5-5 random split, similary to deepcad 120 | random.shuffle(full_uids) # randomly shuffle data 121 | train_uid = full_uids[0:int(len(full_uids)*0.9)] 122 | val_uid = full_uids[int(len(full_uids)*0.9):int(len(full_uids)*0.95)] 123 | test_uid = full_uids[int(len(full_uids)*0.95):] 124 | 125 | train_uid = ['/'.join(uid.split('/')[-2:]) for uid in train_uid] 126 | val_uid = ['/'.join(uid.split('/')[-2:]) for uid in val_uid] 127 | test_uid = ['/'.join(uid.split('/')[-2:]) for uid in test_uid] 128 | 129 | return train_uid, val_uid, test_uid 130 | 131 | 132 | def load_abc_step(root_dir, use_deepcad): 133 | """ 134 | Recursively searches through a given parent directory and its subdirectories 135 | to find the paths of all ABC STEP files. 136 | 137 | Args: 138 | - root_dir (str): Path to the root directory where the search begins. 139 | - use_deepcad (bool): Process deepcad or not 140 | 141 | Returns: 142 | - step_dirs [str]: A list containing the paths to all STEP parent directory 143 | """ 144 | # Load DeepCAD UID 145 | if use_deepcad: 146 | with open('train_val_test_split.json', 'r') as json_file: 147 | deepcad_data = json.load(json_file) 148 | deepcad_data = deepcad_data['train'] + deepcad_data['validation'] + deepcad_data['test'] 149 | deepcad_uid = set([uid.split('/')[1] for uid in deepcad_data]) 150 | 151 | # Create STEP file folder path (based on the default ABC STEP format) 152 | dirs_nested = [[f'{root_dir}/abc_{str(i).zfill(4)}_step_v00']*10000 for i in range(100)] 153 | dirs = [item for sublist in dirs_nested for item in sublist] 154 | subdirs = [f'{str(i).zfill(8)}' for i in range(1000000)] 155 | 156 | if use_deepcad: 157 | step_dirs = [root + '/' + sub for root, sub in zip(dirs, subdirs) if sub in deepcad_uid] 158 | else: 159 | step_dirs = [root + '/' + sub for root, sub in zip(dirs, subdirs)] 160 | 161 | return step_dirs 162 | 163 | 164 | def load_furniture_step(root_dir): 165 | """ 166 | Recursively searches through a given parent directory and its subdirectories 167 | to find the paths of all Furniture STEP files. 168 | 169 | Args: 170 | - root_dir (str): Path to the root directory where the search begins. 171 | 172 | Returns: 173 | - data_files [str]: A list containing the paths to all STEP parent directory 174 | """ 175 | data_files = [] 176 | # Walk through the directory tree starting from the root folder 177 | for root, dirs, files in os.walk(root_dir): 178 | for filename in files: 179 | # Check if the file ends with the specified prefix 180 | if filename.endswith('.step'): 181 | file_path = os.path.join(root, filename) 182 | data_files.append(file_path) 183 | return data_files 184 | 185 | 186 | def update_mapping(data_dict): 187 | """ 188 | Remove unused key index from data dictionary. 189 | """ 190 | dict_new = {} 191 | mapping = {} 192 | max_idx = max(data_dict.keys()) 193 | skipped_indices = np.array(sorted(list(set(np.arange(max_idx)) - set(data_dict.keys())))) 194 | for idx, value in data_dict.items(): 195 | skips = (skipped_indices < idx).sum() 196 | idx_new = idx - skips 197 | dict_new[idx_new] = value 198 | mapping[idx] = idx_new 199 | return dict_new, mapping 200 | 201 | 202 | def face_edge_adj(shape): 203 | """ 204 | *** COPY AND MODIFIED FROM THE ORIGINAL OCCWL SOURCE CODE *** 205 | Extract face/edge geometry and create a face-edge adjacency 206 | graph from the given shape (Solid or Compound) 207 | 208 | Args: 209 | - shape (Shell, Solid, or Compound): Shape 210 | 211 | Returns: 212 | - face_dict: Dictionary of occwl faces, with face ID as the key 213 | - edge_dict: Dictionary of occwl edges, with edge ID as the key 214 | - edgeFace_IncM: Edge ID as the key, Adjacent faces ID as the value 215 | """ 216 | assert isinstance(shape, (Shell, Solid, Compound)) 217 | mapper = EntityMapper(shape) 218 | 219 | ### Faces ### 220 | face_dict = {} 221 | for face in shape.faces(): 222 | face_idx = mapper.face_index(face) 223 | face_dict[face_idx] = (face.surface_type(), face) 224 | 225 | ### Edges and IncidenceMat ### 226 | edgeFace_IncM = {} 227 | edge_dict = {} 228 | for edge in shape.edges(): 229 | if not edge.has_curve(): 230 | continue 231 | 232 | connected_faces = list(shape.faces_from_edge(edge)) 233 | if len(connected_faces) == 2 and not edge.seam(connected_faces[0]) and not edge.seam(connected_faces[1]): 234 | left_face, right_face = edge.find_left_and_right_faces(connected_faces) 235 | if left_face is None or right_face is None: 236 | continue 237 | edge_idx = mapper.edge_index(edge) 238 | edge_dict[edge_idx] = edge 239 | left_index = mapper.face_index(left_face) 240 | right_index = mapper.face_index(right_face) 241 | 242 | if edge_idx in edgeFace_IncM: 243 | edgeFace_IncM[edge_idx] += [left_index, right_index] 244 | else: 245 | edgeFace_IncM[edge_idx] = [left_index, right_index] 246 | else: 247 | pass # ignore seam 248 | 249 | return face_dict, edge_dict, edgeFace_IncM 250 | 251 | 252 | def extract_primitive(solid): 253 | """ 254 | Extract all primitive information from splitted solid 255 | 256 | Args: 257 | - solid (occwl.Solid): A single b-rep solid in occwl format 258 | 259 | Returns: 260 | - face_pnts (N x 32 x 32 x 3): Sampled uv-grid points on the bounded surface region (face) 261 | - edge_pnts (M x 32 x 3): Sampled u-grid points on the boundged curve region (edge) 262 | - edge_corner_pnts (M x 2 x 3): Start & end vertices per edge 263 | - edgeFace_IncM (M x 2): Edge-Face incident matrix, every edge is connect to two face IDs 264 | - faceEdge_IncM: A list of N sublist, where each sublist represents the adjacent edge IDs to a face 265 | """ 266 | assert isinstance(solid, Solid) 267 | 268 | # Retrieve face, edge geometry and face-edge adjacency 269 | face_dict, edge_dict, edgeFace_IncM = face_edge_adj(solid) 270 | 271 | # Skip unused index key, and update the adj 272 | face_dict, face_map = update_mapping(face_dict) 273 | edge_dict, edge_map = update_mapping(edge_dict) 274 | edgeFace_IncM_update = {} 275 | for key, value in edgeFace_IncM.items(): 276 | new_face_indices = [face_map[x] for x in value] 277 | edgeFace_IncM_update[edge_map[key]] = new_face_indices 278 | edgeFace_IncM = edgeFace_IncM_update 279 | 280 | # Face-edge adj 281 | num_faces = len(face_dict) 282 | edgeFace_IncM = np.stack([x for x in edgeFace_IncM.values()]) 283 | faceEdge_IncM = [] 284 | for surf_idx in range(num_faces): 285 | surf_edges, _ = np.where(edgeFace_IncM == surf_idx) 286 | faceEdge_IncM.append(surf_edges) 287 | 288 | # Sample uv-grid from surface (32x32) 289 | graph_face_feat = {} 290 | for face_idx, face_feature in face_dict.items(): 291 | _, face = face_feature 292 | points = uvgrid( 293 | face, method="point", num_u=32, num_v=32 294 | ) 295 | visibility_status = uvgrid( 296 | face, method="visibility_status", num_u=32, num_v=32 297 | ) 298 | mask = np.logical_or(visibility_status == 0, visibility_status == 2) # 0: Inside, 1: Outside, 2: On boundary 299 | # Concatenate channel-wise to form face feature tensor 300 | face_feat = np.concatenate((points, mask), axis=-1) 301 | graph_face_feat[face_idx] = face_feat 302 | face_pnts = np.stack([x for x in graph_face_feat.values()])[:,:,:,:3] 303 | 304 | # sample u-grid from curve (1x32) 305 | graph_edge_feat = {} 306 | graph_corner_feat = {} 307 | for edge_idx, edge in edge_dict.items(): 308 | points = ugrid(edge, method="point", num_u=32) 309 | graph_edge_feat[edge_idx] = points 310 | #### edge corners as start/end vertex ### 311 | v_start = points[0] 312 | v_end = points[-1] 313 | graph_corner_feat[edge_idx] = (v_start, v_end) 314 | edge_pnts = np.stack([x for x in graph_edge_feat.values()]) 315 | edge_corner_pnts = np.stack([x for x in graph_corner_feat.values()]) 316 | 317 | return [face_pnts, edge_pnts, edge_corner_pnts, edgeFace_IncM, faceEdge_IncM] -------------------------------------------------------------------------------- /pc_metric.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import argparse 3 | import os 4 | import numpy as np 5 | from tqdm import tqdm 6 | import random 7 | import warnings 8 | from scipy.stats import entropy 9 | from sklearn.neighbors import NearestNeighbors 10 | from plyfile import PlyData 11 | from pathlib import Path 12 | import multiprocessing 13 | from chamfer_distance import ChamferDistance 14 | 15 | N_POINTS = 2000 16 | 17 | 18 | def find_files(folder, extension): 19 | return sorted([Path(os.path.join(folder, f)) for f in os.listdir(folder) if f.endswith(extension)]) 20 | 21 | 22 | def read_ply(path): 23 | with open(path, 'rb') as f: 24 | plydata = PlyData.read(f) 25 | x = np.array(plydata['vertex']['x']) 26 | y = np.array(plydata['vertex']['y']) 27 | z = np.array(plydata['vertex']['z']) 28 | vertex = np.stack([x, y, z], axis=1) 29 | return vertex 30 | 31 | 32 | def distChamfer(a, b): 33 | x, y = a, b 34 | bs, num_points, points_dim = x.size() 35 | xx = torch.bmm(x, x.transpose(2, 1)) 36 | yy = torch.bmm(y, y.transpose(2, 1)) 37 | zz = torch.bmm(x, y.transpose(2, 1)) 38 | diag_ind = torch.arange(0, num_points).to(a).long() 39 | rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx) 40 | ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy) 41 | P = (rx.transpose(2, 1) + ry - 2 * zz) 42 | return P.min(1)[0], P.min(2)[0] 43 | 44 | 45 | def _pairwise_CD(sample_pcs, ref_pcs, batch_size): 46 | N_sample = sample_pcs.shape[0] 47 | N_ref = ref_pcs.shape[0] 48 | all_cd = [] 49 | all_emd = [] 50 | iterator = range(N_sample) 51 | matched_gt = [] 52 | pbar = tqdm(iterator) 53 | chamfer_dist = ChamferDistance() 54 | 55 | for sample_b_start in pbar: 56 | sample_batch = sample_pcs[sample_b_start] 57 | 58 | cd_lst = [] 59 | emd_lst = [] 60 | for ref_b_start in range(0, N_ref, batch_size): 61 | ref_b_end = min(N_ref, ref_b_start + batch_size) 62 | ref_batch = ref_pcs[ref_b_start:ref_b_end] 63 | 64 | batch_size_ref = ref_batch.size(0) 65 | sample_batch_exp = sample_batch.view(1, -1, 3).expand(batch_size_ref, -1, -1) 66 | sample_batch_exp = sample_batch_exp.contiguous() 67 | 68 | dl, dr, idx1, idx2 = chamfer_dist(sample_batch_exp,ref_batch) 69 | cd_lst.append((dl.mean(dim=1) + dr.mean(dim=1)).view(1, -1)) 70 | 71 | cd_lst = torch.cat(cd_lst, dim=1) 72 | all_cd.append(cd_lst) 73 | 74 | hit = np.argmin(cd_lst.detach().cpu().numpy()[0]) 75 | matched_gt.append(hit) 76 | pbar.set_postfix({"cov": len(np.unique(matched_gt)) * 1.0 / N_ref}) 77 | 78 | all_cd = torch.cat(all_cd, dim=0) # N_sample, N_ref 79 | 80 | return all_cd 81 | 82 | 83 | def compute_cov_mmd(sample_pcs, ref_pcs, batch_size): 84 | all_dist = _pairwise_CD(sample_pcs, ref_pcs, batch_size) 85 | N_sample, N_ref = all_dist.size(0), all_dist.size(1) 86 | min_val_fromsmp, min_idx = torch.min(all_dist, dim=1) 87 | min_val, _ = torch.min(all_dist, dim=0) 88 | mmd = min_val.mean() 89 | cov = float(min_idx.unique().view(-1).size(0)) / float(N_ref) 90 | cov = torch.tensor(cov).to(all_dist) 91 | 92 | return { 93 | 'MMD-CD': mmd.item(), 94 | 'COV-CD': cov.item(), 95 | } 96 | 97 | 98 | def jsd_between_point_cloud_sets(sample_pcs, ref_pcs, in_unit_sphere, resolution=28): 99 | '''Computes the JSD between two sets of point-clouds, as introduced in the paper ```Learning Representations And Generative Models For 3D Point Clouds```. 100 | Args: 101 | sample_pcs: (np.ndarray S1xR2x3) S1 point-clouds, each of R1 points. 102 | ref_pcs: (np.ndarray S2xR2x3) S2 point-clouds, each of R2 points. 103 | resolution: (int) grid-resolution. Affects granularity of measurements. 104 | ''' 105 | sample_grid_var = entropy_of_occupancy_grid(sample_pcs, resolution, in_unit_sphere)[1] 106 | ref_grid_var = entropy_of_occupancy_grid(ref_pcs, resolution, in_unit_sphere)[1] 107 | return jensen_shannon_divergence(sample_grid_var, ref_grid_var) 108 | 109 | 110 | def entropy_of_occupancy_grid(pclouds, grid_resolution, in_sphere=False): 111 | '''Given a collection of point-clouds, estimate the entropy of the random variables 112 | corresponding to occupancy-grid activation patterns. 113 | Inputs: 114 | pclouds: (numpy array) #point-clouds x points per point-cloud x 3 115 | grid_resolution (int) size of occupancy grid that will be used. 116 | ''' 117 | epsilon = 10e-4 118 | bound = 1 + epsilon 119 | if abs(np.max(pclouds)) > bound or abs(np.min(pclouds)) > bound: 120 | print(abs(np.max(pclouds)), abs(np.min(pclouds))) 121 | warnings.warn('Point-clouds are not in unit cube.') 122 | 123 | if in_sphere and np.max(np.sqrt(np.sum(pclouds ** 2, axis=2))) > bound: 124 | warnings.warn('Point-clouds are not in unit sphere.') 125 | 126 | grid_coordinates, _ = unit_cube_grid_point_cloud(grid_resolution, in_sphere) 127 | grid_coordinates = grid_coordinates.reshape(-1, 3) 128 | grid_counters = np.zeros(len(grid_coordinates)) 129 | grid_bernoulli_rvars = np.zeros(len(grid_coordinates)) 130 | nn = NearestNeighbors(n_neighbors=1).fit(grid_coordinates) 131 | 132 | for pc in pclouds: 133 | _, indices = nn.kneighbors(pc) 134 | indices = np.squeeze(indices) 135 | for i in indices: 136 | grid_counters[i] += 1 137 | indices = np.unique(indices) 138 | for i in indices: 139 | grid_bernoulli_rvars[i] += 1 140 | 141 | acc_entropy = 0.0 142 | n = float(len(pclouds)) 143 | for g in grid_bernoulli_rvars: 144 | p = 0.0 145 | if g > 0: 146 | p = float(g) / n 147 | acc_entropy += entropy([p, 1.0 - p]) 148 | 149 | return acc_entropy / len(grid_counters), grid_counters 150 | 151 | 152 | def unit_cube_grid_point_cloud(resolution, clip_sphere=False): 153 | '''Returns the center coordinates of each cell of a 3D grid with resolution^3 cells, 154 | that is placed in the unit-cube. 155 | If clip_sphere it True it drops the "corner" cells that lie outside the unit-sphere. 156 | ''' 157 | grid = np.ndarray((resolution, resolution, resolution, 3), np.float32) 158 | spacing = 1.0 / float(resolution - 1) * 2 159 | for i in range(resolution): 160 | for j in range(resolution): 161 | for k in range(resolution): 162 | grid[i, j, k, 0] = i * spacing - 0.5 * 2 163 | grid[i, j, k, 1] = j * spacing - 0.5 * 2 164 | grid[i, j, k, 2] = k * spacing - 0.5 * 2 165 | 166 | if clip_sphere: 167 | grid = grid.reshape(-1, 3) 168 | grid = grid[np.linalg.norm(grid, axis=1) <= 0.5] 169 | 170 | return grid, spacing 171 | 172 | 173 | def jensen_shannon_divergence(P, Q): 174 | if np.any(P < 0) or np.any(Q < 0): 175 | raise ValueError('Negative values.') 176 | if len(P) != len(Q): 177 | raise ValueError('Non equal size.') 178 | 179 | P_ = P / np.sum(P) # Ensure probabilities. 180 | Q_ = Q / np.sum(Q) 181 | 182 | e1 = entropy(P_, base=2) 183 | e2 = entropy(Q_, base=2) 184 | e_sum = entropy((P_ + Q_) / 2.0, base=2) 185 | res = e_sum - ((e1 + e2) / 2.0) 186 | 187 | res2 = _jsdiv(P_, Q_) 188 | 189 | if not np.allclose(res, res2, atol=10e-5, rtol=0): 190 | warnings.warn('Numerical values of two JSD methods don\'t agree.') 191 | 192 | return res 193 | 194 | 195 | def _jsdiv(P, Q): 196 | '''another way of computing JSD''' 197 | 198 | def _kldiv(A, B): 199 | a = A.copy() 200 | b = B.copy() 201 | idx = np.logical_and(a > 0, b > 0) 202 | a = a[idx] 203 | b = b[idx] 204 | return np.sum([v for v in a * np.log2(a / b)]) 205 | 206 | P_ = P / np.sum(P) 207 | Q_ = Q / np.sum(Q) 208 | 209 | M = 0.5 * (P_ + Q_) 210 | 211 | return 0.5 * (_kldiv(P_, M) + _kldiv(Q_, M)) 212 | 213 | 214 | def downsample_pc(points, n): 215 | sample_idx = random.sample(list(range(points.shape[0])), n) 216 | return points[sample_idx] 217 | 218 | 219 | def normalize_pc(points): 220 | # normalize 221 | mean = np.mean(points, axis=0) 222 | points = (points - mean) 223 | # fit to unit cube 224 | scale = np.max(np.abs(points)) 225 | points = points / scale 226 | return points 227 | 228 | 229 | def collect_pc(cad_folder): 230 | pc_path = find_files(os.path.join(cad_folder, 'pcd'), 'final_pcd.ply') 231 | if len(pc_path) == 0: 232 | return [] 233 | pc_path = pc_path[-1] # final pcd 234 | pc = read_ply(pc_path) 235 | if pc.shape[0] > N_POINTS: 236 | pc = downsample_pc(pc, N_POINTS) 237 | pc = normalize_pc(pc) 238 | return pc 239 | 240 | def collect_pc2(cad_folder): 241 | pc = read_ply(cad_folder) 242 | if pc.shape[0] > N_POINTS: 243 | pc = downsample_pc(pc, N_POINTS) 244 | pc = normalize_pc(pc) 245 | return pc 246 | 247 | theta_x = np.radians(90) # Rotation angle around X-axis 248 | theta_y = np.radians(90) # Rotation angle around Y-axis 249 | theta_z = np.radians(180) # Rotation angle around Z-axis 250 | 251 | # Create individual rotation matrices 252 | Rx = np.array([[1, 0, 0], 253 | [0, np.cos(theta_x), -np.sin(theta_x)], 254 | [0, np.sin(theta_x), np.cos(theta_x)]]) 255 | 256 | Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)], 257 | [0, 1, 0], 258 | [-np.sin(theta_y), 0, np.cos(theta_y)]]) 259 | 260 | Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0], 261 | [np.sin(theta_z), np.cos(theta_z), 0], 262 | [0, 0, 1]]) 263 | 264 | rotation_matrix = np.dot(np.dot(Rz, Ry), Rx) 265 | 266 | def collect_pc3(cad_folder): 267 | pc = read_ply(cad_folder) 268 | if pc.shape[0] > N_POINTS: 269 | pc = downsample_pc(pc, N_POINTS) 270 | pc = normalize_pc(pc) 271 | rotated_point_cloud = np.dot(pc, rotation_matrix.T).astype(np.float32) # Transpose the rotation matrix to apply it correctly 272 | return rotated_point_cloud 273 | 274 | def load_data_with_prefix(root_folder, prefix): 275 | data_files = [] 276 | 277 | # Walk through the directory tree starting from the root folder 278 | for root, dirs, files in os.walk(root_folder): 279 | for filename in files: 280 | # Check if the file ends with the specified prefix 281 | if filename.endswith(prefix): 282 | file_path = os.path.join(root, filename) 283 | data_files.append(file_path) 284 | 285 | return data_files 286 | 287 | def main(): 288 | parser = argparse.ArgumentParser() 289 | parser.add_argument("--fake", type=str) 290 | parser.add_argument("--real", type=str) 291 | parser.add_argument("--n_test", type=int, default=1000) 292 | parser.add_argument("--multi", type=int, default=3) 293 | parser.add_argument("--times", type=int, default=10) 294 | parser.add_argument("--batch_size", type=int, default=64) 295 | args = parser.parse_args() 296 | 297 | print("n_test: {}, multiplier: {}, repeat times: {}".format(args.n_test, args.multi, args.times)) 298 | 299 | args.output = args.fake + '_results.txt' 300 | 301 | # Load reference pcd 302 | num_cpus = multiprocessing.cpu_count() 303 | ref_pcs = [] 304 | shape_paths = load_data_with_prefix(args.real, '.ply') 305 | load_iter = multiprocessing.Pool(num_cpus).imap(collect_pc2, shape_paths) 306 | for pc in tqdm(load_iter, total=len(shape_paths)): 307 | if len(pc) > 0: 308 | ref_pcs.append(pc) 309 | ref_pcs = np.stack(ref_pcs, axis=0) 310 | print("real point clouds: {}".format(ref_pcs.shape)) 311 | 312 | 313 | # Load fake pcd 314 | sample_pcs = [] 315 | shape_paths = load_data_with_prefix(args.fake, '.ply') 316 | load_iter = multiprocessing.Pool(num_cpus).imap(collect_pc2, shape_paths) 317 | for pc in tqdm(load_iter, total=len(shape_paths)): 318 | if len(pc) > 0: 319 | sample_pcs.append(pc) 320 | sample_pcs = np.stack(sample_pcs, axis=0) 321 | 322 | print("fake point clouds: {}".format(sample_pcs.shape)) 323 | 324 | # Testing 325 | fp = open(args.output, "w") 326 | result_list = [] 327 | for i in range(args.times): 328 | print("iteration {}...".format(i)) 329 | select_idx = random.sample(list(range(len(sample_pcs))), int(args.multi * args.n_test)) 330 | rand_sample_pcs = sample_pcs[select_idx] 331 | 332 | select_idx = random.sample(list(range(len(ref_pcs))), args.n_test) 333 | rand_ref_pcs = ref_pcs[select_idx] 334 | 335 | jsd = jsd_between_point_cloud_sets(rand_sample_pcs, rand_ref_pcs, in_unit_sphere=False) 336 | with torch.no_grad(): 337 | rand_sample_pcs = torch.tensor(rand_sample_pcs).cuda() 338 | rand_ref_pcs = torch.tensor(rand_ref_pcs).cuda() 339 | result = compute_cov_mmd(rand_sample_pcs, rand_ref_pcs, batch_size=args.batch_size) 340 | result.update({"JSD": jsd}) 341 | 342 | print(result) 343 | print(result, file=fp) 344 | result_list.append(result) 345 | avg_result = {} 346 | for k in result_list[0].keys(): 347 | avg_result.update({"avg-" + k: np.mean([x[k] for x in result_list])}) 348 | print("average result:") 349 | print(avg_result) 350 | print(avg_result, file=fp) 351 | fp.close() 352 | 353 | 354 | if __name__ == '__main__': 355 | main() -------------------------------------------------------------------------------- /sample.py: -------------------------------------------------------------------------------- 1 | import os 2 | import yaml 3 | import torch 4 | import argparse 5 | import numpy as np 6 | from tqdm import tqdm 7 | from network import * 8 | from diffusers import DDPMScheduler, PNDMScheduler 9 | from OCC.Extend.DataExchange import write_stl_file, write_step_file 10 | from utils import ( 11 | randn_tensor, 12 | compute_bbox_center_and_size, 13 | generate_random_string, 14 | construct_brep, 15 | detect_shared_vertex, 16 | detect_shared_edge, 17 | joint_optimize 18 | ) 19 | 20 | 21 | text2int = {'uncond':0, 22 | 'bathtub':1, 23 | 'bed':2, 24 | 'bench':3, 25 | 'bookshelf':4, 26 | 'cabinet':5, 27 | 'chair':6, 28 | 'couch':7, 29 | 'lamp':8, 30 | 'sofa':9, 31 | 'table':10 32 | } 33 | 34 | 35 | def sample(eval_args): 36 | 37 | # Inference configuration 38 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 39 | batch_size = eval_args['batch_size'] 40 | z_threshold = eval_args['z_threshold'] 41 | bbox_threshold =eval_args['bbox_threshold'] 42 | save_folder = eval_args['save_folder'] 43 | num_surfaces = eval_args['num_surfaces'] 44 | num_edges = eval_args['num_edges'] 45 | 46 | if eval_args['use_cf']: 47 | class_label = torch.LongTensor([text2int[eval_args['class_label']]]*batch_size + \ 48 | [text2int['uncond']]*batch_size).cuda().reshape(-1,1) 49 | w = 0.6 50 | else: 51 | class_label = None 52 | 53 | if not os.path.exists(save_folder): 54 | os.makedirs(save_folder) 55 | 56 | surfPos_model = SurfPosNet(eval_args['use_cf']) 57 | surfPos_model.load_state_dict(torch.load(eval_args['surfpos_weight'])) 58 | surfPos_model = surfPos_model.to(device).eval() 59 | 60 | surfZ_model = SurfZNet(eval_args['use_cf']) 61 | surfZ_model.load_state_dict(torch.load(eval_args['surfz_weight'])) 62 | surfZ_model = surfZ_model.to(device).eval() 63 | 64 | edgePos_model = EdgePosNet(eval_args['use_cf']) 65 | edgePos_model.load_state_dict(torch.load(eval_args['edgepos_weight'])) 66 | edgePos_model = edgePos_model.to(device).eval() 67 | 68 | edgeZ_model = EdgeZNet(eval_args['use_cf']) 69 | edgeZ_model.load_state_dict(torch.load(eval_args['edgez_weight'])) 70 | edgeZ_model = edgeZ_model.to(device).eval() 71 | 72 | surf_vae = AutoencoderKLFastDecode(in_channels=3, 73 | out_channels=3, 74 | down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D'], 75 | up_block_types= ['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'], 76 | block_out_channels=[128, 256, 512, 512], 77 | layers_per_block=2, 78 | act_fn='silu', 79 | latent_channels=3, 80 | norm_num_groups=32, 81 | sample_size=512, 82 | ) 83 | surf_vae.load_state_dict(torch.load(eval_args['surfvae_weight']), strict=False) 84 | surf_vae = surf_vae.to(device).eval() 85 | 86 | edge_vae = AutoencoderKL1DFastDecode( 87 | in_channels=3, 88 | out_channels=3, 89 | down_block_types=['DownBlock1D', 'DownBlock1D', 'DownBlock1D'], 90 | up_block_types=['UpBlock1D', 'UpBlock1D', 'UpBlock1D'], 91 | block_out_channels=[128, 256, 512], 92 | layers_per_block=2, 93 | act_fn='silu', 94 | latent_channels=3, 95 | norm_num_groups=32, 96 | sample_size=512 97 | ) 98 | edge_vae.load_state_dict(torch.load(eval_args['edgevae_weight']), strict=False) 99 | edge_vae = edge_vae.to(device).eval() 100 | 101 | pndm_scheduler = PNDMScheduler( 102 | num_train_timesteps=1000, 103 | beta_schedule='linear', 104 | prediction_type='epsilon', 105 | beta_start = 0.0001, 106 | beta_end = 0.02, 107 | ) 108 | 109 | ddpm_scheduler = DDPMScheduler( 110 | num_train_timesteps=1000, 111 | beta_schedule='linear', 112 | prediction_type='epsilon', 113 | beta_start = 0.0001, 114 | beta_end = 0.02, 115 | clip_sample = True, 116 | clip_sample_range=3 117 | ) 118 | 119 | 120 | with torch.no_grad(): 121 | with torch.cuda.amp.autocast(): 122 | 123 | ########################################### 124 | # STEP 1-1: generate the surface position # 125 | ########################################### 126 | surfPos = randn_tensor((batch_size, num_surfaces, 6)).to(device) 127 | 128 | pndm_scheduler.set_timesteps(200) 129 | for t in tqdm(pndm_scheduler.timesteps[:158]):# 130 | timesteps = t.reshape(-1).cuda() 131 | if class_label is not None: 132 | _surfPos_ = surfPos.repeat(2,1,1) 133 | pred = surfPos_model(_surfPos_, timesteps, class_label) 134 | pred = pred[:batch_size] * (1+w) - pred[batch_size:] * w 135 | else: 136 | pred = surfPos_model(surfPos, timesteps, class_label) 137 | surfPos = pndm_scheduler.step(pred, t, surfPos).prev_sample 138 | 139 | # Late increase for ABC/DeepCAD (slightly more efficient) 140 | if not eval_args['use_cf']: 141 | surfPos = surfPos.repeat(1,2,1) 142 | num_surfaces *= 2 143 | 144 | ddpm_scheduler.set_timesteps(1000) 145 | for t in tqdm(ddpm_scheduler.timesteps[-250:]): 146 | timesteps = t.reshape(-1).cuda() 147 | if class_label is not None: 148 | _surfPos_ = surfPos.repeat(2,1,1) 149 | pred = surfPos_model(_surfPos_, timesteps, class_label) 150 | pred = pred[:batch_size] * (1+w) - pred[batch_size:] * w 151 | else: 152 | pred = surfPos_model(surfPos, timesteps, class_label) 153 | surfPos = ddpm_scheduler.step(pred, t, surfPos).prev_sample 154 | 155 | 156 | ####################################### 157 | # STEP 1-2: remove duplicate surfaces # 158 | ####################################### 159 | surfPos_deduplicate = [] 160 | surfMask_deduplicate = [] 161 | for ii in range(batch_size): 162 | bboxes = np.round(surfPos[ii].unflatten(-1,torch.Size([2,3])).detach().cpu().numpy(), 4) 163 | non_repeat = bboxes[:1] 164 | for bbox_idx, bbox in enumerate(bboxes): 165 | diff = np.max(np.max(np.abs(non_repeat - bbox),-1),-1) 166 | same = diff < bbox_threshold 167 | bbox_rev = bbox[::-1] # also test reverse bbox for matching 168 | diff_rev = np.max(np.max(np.abs(non_repeat - bbox_rev),-1),-1) 169 | same_rev = diff_rev < bbox_threshold 170 | if same.sum()>=1 or same_rev.sum()>=1: 171 | continue # repeat value 172 | else: 173 | non_repeat = np.concatenate([non_repeat, bbox[np.newaxis,:,:]],0) 174 | bboxes = non_repeat.reshape(len(non_repeat),-1) 175 | 176 | surf_mask = torch.zeros((1, len(bboxes))) == 1 177 | bbox_padded = torch.concat([torch.FloatTensor(bboxes), torch.zeros(num_surfaces-len(bboxes),6)]) 178 | mask_padded = torch.concat([surf_mask, torch.zeros(1, num_surfaces-len(bboxes))==0], -1) 179 | surfPos_deduplicate.append(bbox_padded) 180 | surfMask_deduplicate.append(mask_padded) 181 | 182 | surfPos = torch.stack(surfPos_deduplicate).cuda() 183 | surfMask = torch.vstack(surfMask_deduplicate).cuda() 184 | 185 | 186 | ################################# 187 | # STEP 1-3: generate surface z # 188 | ################################# 189 | surfZ = randn_tensor((batch_size, num_surfaces, 48)).to(device) 190 | 191 | pndm_scheduler.set_timesteps(200) 192 | for t in tqdm(pndm_scheduler.timesteps): 193 | timesteps = t.reshape(-1).cuda() 194 | if class_label is not None: 195 | _surfZ_ = surfZ.repeat(2,1,1) 196 | _surfPos_ = surfPos.repeat(2,1,1) 197 | _surfMask_ = surfMask.repeat(2,1) 198 | pred = surfZ_model(_surfZ_, timesteps, _surfPos_, _surfMask_, class_label) 199 | pred = pred[:batch_size] * (1+w) - pred[batch_size:] * w 200 | else: 201 | pred = surfZ_model(surfZ, timesteps, surfPos, surfMask, class_label) 202 | surfZ = pndm_scheduler.step(pred, t, surfZ).prev_sample 203 | 204 | 205 | ######################################## 206 | # STEP 2-1: generate the edge position # 207 | ######################################## 208 | edgePos = randn_tensor((batch_size, num_surfaces, num_edges, 6)).cuda() 209 | 210 | pndm_scheduler.set_timesteps(200) 211 | for t in tqdm(pndm_scheduler.timesteps[:158]): 212 | timesteps = t.reshape(-1).cuda() 213 | if class_label is not None: 214 | _surfZ_ = surfZ.repeat(2,1,1) 215 | _surfPos_ = surfPos.repeat(2,1,1) 216 | _surfMask_ = surfMask.repeat(2,1) 217 | _edgePos_ = edgePos.repeat(2,1,1,1) 218 | noise_pred = edgePos_model(_edgePos_, timesteps, _surfPos_, _surfZ_, _surfMask_, class_label) 219 | noise_pred = noise_pred[:batch_size] * (1+w) - noise_pred[batch_size:] * w 220 | else: 221 | noise_pred = edgePos_model(edgePos, timesteps, surfPos, surfZ, surfMask, class_label) 222 | edgePos = pndm_scheduler.step(noise_pred, t, edgePos).prev_sample 223 | 224 | ddpm_scheduler.set_timesteps(1000) 225 | for t in tqdm(ddpm_scheduler.timesteps[-250:]): 226 | timesteps = t.reshape(-1).cuda() 227 | if class_label is not None: 228 | _surfZ_ = surfZ.repeat(2,1,1) 229 | _surfPos_ = surfPos.repeat(2,1,1) 230 | _surfMask_ = surfMask.repeat(2,1) 231 | _edgePos_ = edgePos.repeat(2,1,1,1) 232 | noise_pred = edgePos_model(_edgePos_, timesteps, _surfPos_, _surfZ_, _surfMask_, class_label) 233 | noise_pred = noise_pred[:batch_size] * (1+w) - noise_pred[batch_size:] * w 234 | else: 235 | noise_pred = edgePos_model(edgePos, timesteps, surfPos, surfZ, surfMask, class_label) 236 | edgePos = ddpm_scheduler.step(noise_pred, t, edgePos).prev_sample 237 | 238 | 239 | #################################################### 240 | # STEP 2-2: remove duplicate edges per face (bbox) # 241 | #################################################### 242 | edgeM = surfMask.unsqueeze(-1).repeat(1, 1, num_edges) 243 | 244 | for ii in range(batch_size): 245 | edge_bboxs = edgePos[ii][~surfMask[ii]].detach().cpu().numpy() 246 | 247 | for surf_idx, bboxes in enumerate(edge_bboxs): 248 | bboxes = bboxes.reshape(len(bboxes),2,3) 249 | valid_bbox = bboxes[0:1] 250 | for bbox_idx, bbox in enumerate(bboxes): 251 | diff = np.max(np.max(np.abs(valid_bbox - bbox),-1),-1) 252 | bbox_rev = bbox[::-1] # also test reverse bbox for matching 253 | diff_rev = np.max(np.max(np.abs(valid_bbox - bbox_rev),-1),-1) 254 | same = diff < bbox_threshold 255 | same_rev = diff_rev < bbox_threshold 256 | if same.sum()>=1 or same_rev.sum()>=1: 257 | edgeM[ii, surf_idx, bbox_idx] = True 258 | continue # repeat value 259 | else: 260 | valid_bbox = np.concatenate([valid_bbox, bbox[np.newaxis,:,:]],0) 261 | edgeM[ii, surf_idx, 0] = False # set first one to False 262 | 263 | 264 | ############################## 265 | # STEP 2-3: generate edge zv # 266 | ############################## 267 | edgeZV = randn_tensor((batch_size, num_surfaces, num_edges, 18)).cuda() 268 | 269 | pndm_scheduler.set_timesteps(200) 270 | for t in tqdm(pndm_scheduler.timesteps): 271 | timesteps = t.reshape(-1).cuda() 272 | if class_label is not None: 273 | _surfZ_ = surfZ.repeat(2,1,1) 274 | _surfPos_ = surfPos.repeat(2,1,1) 275 | _edgePos_ = edgePos.repeat(2,1,1,1) 276 | _edgeM_ = edgeM.repeat(2,1,1) 277 | _edgeZV_ = edgeZV.repeat(2,1,1,1) 278 | noise_pred = edgeZ_model(_edgeZV_, timesteps, _edgePos_, _surfPos_, _surfZ_, _edgeM_, class_label) 279 | noise_pred = noise_pred[:batch_size] * (1+w) - noise_pred[batch_size:] * w 280 | else: 281 | noise_pred = edgeZ_model(edgeZV, timesteps, edgePos, surfPos, surfZ, edgeM, class_label) 282 | edgeZV = pndm_scheduler.step(noise_pred, t, edgeZV).prev_sample 283 | 284 | edgeZV[edgeM] = 0 # set removed data to 0 285 | edge_z = edgeZV[:,:,:,:12] 286 | edgeV = edgeZV[:,:,:,12:].detach().cpu().numpy() 287 | 288 | # Decode the surfaces 289 | surf_ncs = surf_vae(surfZ.unflatten(-1,torch.Size([16,3])).flatten(0,1).permute(0,2,1).unflatten(-1,torch.Size([4,4]))) 290 | surf_ncs = surf_ncs.permute(0,2,3,1).unflatten(0, torch.Size([batch_size, num_surfaces])).detach().cpu().numpy() 291 | 292 | # Decode the edges 293 | edge_ncs = edge_vae(edge_z.unflatten(-1,torch.Size([4,3])).reshape(-1,4,3).permute(0,2,1)) 294 | edge_ncs = edge_ncs.permute(0,2,1).reshape(batch_size, num_surfaces, num_edges, 32, 3).detach().cpu().numpy() 295 | 296 | 297 | edge_mask = edgeM.detach().cpu().numpy() 298 | edge_pos = edgePos.detach().cpu().numpy() / 3.0 299 | surfPos = surfPos.detach().cpu().numpy() / 3.0 300 | 301 | 302 | ############################################# 303 | ### STEP 3: Post-process (per-single CAD) ### 304 | ############################################# 305 | for batch_idx in range(batch_size): 306 | # Per cad (not including invalid faces) 307 | surfMask_cad = surfMask[batch_idx].detach().cpu().numpy() 308 | edge_mask_cad = edge_mask[batch_idx][~surfMask_cad] 309 | edge_pos_cad = edge_pos[batch_idx][~surfMask_cad] 310 | edge_ncs_cad = edge_ncs[batch_idx][~surfMask_cad] 311 | edgeV_cad = edgeV[batch_idx][~surfMask_cad] 312 | edge_z_cad = edge_z[batch_idx][~surfMask[batch_idx]].detach().cpu().numpy()[~edge_mask_cad] 313 | surf_z_cad = surfZ[batch_idx][~surfMask[batch_idx]].detach().cpu().numpy() 314 | surf_pos_cad = surfPos[batch_idx][~surfMask_cad] 315 | 316 | # Retrieve vertices based on edge start/end 317 | edgeV_bbox = [] 318 | for bbox, ncs, mask in zip(edge_pos_cad, edge_ncs_cad, edge_mask_cad): 319 | epos = bbox[~mask] 320 | edge = ncs[~mask] 321 | bbox_startends = [] 322 | for bb, ee in zip(epos, edge): 323 | bcenter, bsize = compute_bbox_center_and_size(bb[0:3], bb[3:]) 324 | wcs = ee*(bsize/2) + bcenter 325 | bbox_start_end = wcs[[0,-1]] 326 | bbox_start_end = bbox_start_end.reshape(2,3) 327 | bbox_startends.append(bbox_start_end.reshape(1,2,3)) 328 | bbox_startends = np.vstack(bbox_startends) 329 | edgeV_bbox.append(bbox_startends) 330 | 331 | ### 3-1: Detect shared vertices ### 332 | try: 333 | unique_vertices, new_vertex_dict = detect_shared_vertex(edgeV_cad, edge_mask_cad, edgeV_bbox) 334 | except Exception as e: 335 | print('Vertex detection failed...') 336 | continue 337 | 338 | ### 3-2: Detect shared edges ### 339 | try: 340 | unique_faces, unique_edges, FaceEdgeAdj, EdgeVertexAdj = detect_shared_edge(unique_vertices, new_vertex_dict, edge_z_cad, surf_z_cad, z_threshold, edge_mask_cad) 341 | except Exception as e: 342 | print('Edge detection failed...') 343 | continue 344 | 345 | # Decode unique faces / edges 346 | with torch.no_grad(): 347 | with torch.cuda.amp.autocast(): 348 | surf_ncs_cad = surf_vae(torch.FloatTensor(unique_faces).cuda().unflatten(-1,torch.Size([16,3])).permute(0,2,1).unflatten(-1,torch.Size([4,4]))) 349 | surf_ncs_cad = surf_ncs_cad.permute(0,2,3,1).detach().cpu().numpy() 350 | edge_ncs_cad = edge_vae(torch.FloatTensor(unique_edges).cuda().unflatten(-1,torch.Size([4,3])).permute(0,2,1)) 351 | edge_ncs_cad = edge_ncs_cad.permute(0,2,1).detach().cpu().numpy() 352 | 353 | #### 3-3: Joint Optimize ### 354 | num_edge = len(edge_ncs_cad) 355 | num_surf = len(surf_ncs_cad) 356 | surf_wcs, edge_wcs = joint_optimize(surf_ncs_cad, edge_ncs_cad, surf_pos_cad, unique_vertices, EdgeVertexAdj, FaceEdgeAdj, num_edge, num_surf) 357 | 358 | #### 3-4: Build the B-rep ### 359 | try: 360 | solid = construct_brep(surf_wcs, edge_wcs, FaceEdgeAdj, EdgeVertexAdj) 361 | except Exception as e: 362 | print('B-rep rebuild failed...') 363 | continue 364 | 365 | # Save CAD model 366 | random_string = generate_random_string(15) 367 | write_step_file(solid, f'{save_folder}/{random_string}_{batch_idx}.step') 368 | write_stl_file(solid, f'{save_folder}/{random_string}_{batch_idx}.stl', linear_deflection=0.001, angular_deflection=0.5) 369 | return 370 | 371 | 372 | if __name__ == "__main__": 373 | parser = argparse.ArgumentParser() 374 | parser.add_argument("--mode", type=str, choices=['abc', 'deepcad', 'furniture'], default='abc', 375 | help="Choose between evaluation mode [abc/deepcad/furniture] (default: abc)") 376 | args = parser.parse_args() 377 | 378 | # Load evaluation config 379 | with open('eval_config.yaml', 'r') as file: 380 | config = yaml.safe_load(file) 381 | eval_args = config[args.mode] 382 | 383 | while(True): 384 | sample(eval_args) -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | import os 2 | import math 3 | import pickle 4 | import torch 5 | import numpy as np 6 | from tqdm import tqdm 7 | import random 8 | from multiprocessing.pool import Pool 9 | from utils import ( 10 | rotate_point_cloud, 11 | bbox_corners, 12 | rotate_axis, 13 | get_bbox, 14 | pad_repeat, 15 | pad_zero, 16 | ) 17 | 18 | # furniture class labels 19 | text2int = {'bathtub':0, 'bed':1, 'bench':2, 'bookshelf':3,'cabinet':4, 'chair':5, 'couch':6, 'lamp':7, 'sofa':8, 'table':9} 20 | 21 | 22 | def filter_data(data): 23 | """ 24 | Helper function to check if a brep needs to be included 25 | in the training data or not 26 | """ 27 | data_path, max_face, max_edge, scaled_value, threshold_value, data_class = data 28 | # Load data 29 | with open(data_path, "rb") as tf: 30 | data = pickle.load(tf) 31 | _, _, _, _, _, _, _, faceEdge_adj, surf_bbox, edge_bbox, _, _ = data.values() 32 | 33 | skip = False 34 | 35 | # Skip over max size data 36 | if len(surf_bbox)>max_face: 37 | skip = True 38 | 39 | for surf_edges in faceEdge_adj: 40 | if len(surf_edges)>max_edge: 41 | skip = True 42 | 43 | # Skip surfaces too close to each other 44 | surf_bbox = surf_bbox * scaled_value # make bbox difference larger 45 | 46 | _surf_bbox_ = surf_bbox.reshape(len(surf_bbox),2,3) 47 | non_repeat = _surf_bbox_[:1] 48 | for bbox in _surf_bbox_: 49 | diff = np.max(np.max(np.abs(non_repeat - bbox),-1),-1) 50 | same = diff < threshold_value 51 | if same.sum()>=1: 52 | continue # repeat value 53 | else: 54 | non_repeat = np.concatenate([non_repeat, bbox[np.newaxis,:,:]],0) 55 | if len(non_repeat) != len(_surf_bbox_): 56 | skip = True 57 | 58 | # Skip edges too close to each other 59 | se_bbox = [] 60 | for adj in faceEdge_adj: 61 | if len(edge_bbox[adj]) == 0: 62 | skip = True 63 | se_bbox.append(edge_bbox[adj] * scaled_value) 64 | 65 | for bbb in se_bbox: 66 | _edge_bbox_ = bbb.reshape(len(bbb),2,3) 67 | non_repeat = _edge_bbox_[:1] 68 | for bbox in _edge_bbox_: 69 | diff = np.max(np.max(np.abs(non_repeat - bbox),-1),-1) 70 | same = diff < threshold_value 71 | if same.sum()>=1: 72 | continue # repeat value 73 | else: 74 | non_repeat = np.concatenate([non_repeat, bbox[np.newaxis,:,:]],0) 75 | if len(non_repeat) != len(_edge_bbox_): 76 | skip = True 77 | 78 | if skip: 79 | return None, None 80 | else: 81 | return data_path, data_class 82 | 83 | 84 | def load_data(input_data, input_list, validate, args): 85 | # Filter data list 86 | with open(input_list, "rb") as tf: 87 | if validate: 88 | data_list = pickle.load(tf)['val'] 89 | else: 90 | data_list = pickle.load(tf)['train'] 91 | 92 | data_paths = [] 93 | data_classes = [] 94 | for uid in data_list: 95 | try: 96 | path = os.path.join(input_data, str(math.floor(int(uid.split('.')[0])/10000)).zfill(4), uid) 97 | class_label = -1 # unconditional generation (abc/deepcad) 98 | except Exception: 99 | path = os.path.join(input_data, uid) 100 | class_label = text2int[uid.split('/')[0]] # conditional generation (furniture) 101 | data_paths.append(path) 102 | data_classes.append(class_label) 103 | 104 | # Filter data in parallel 105 | loaded_data = [] 106 | params = zip(data_paths, [args.max_face]*len(data_list), [args.max_edge]*len(data_list), 107 | [args.bbox_scaled]*len(data_list), [args.threshold]*len(data_list), data_classes) 108 | convert_iter = Pool(os.cpu_count()).imap(filter_data, params) 109 | for data_path, data_class in tqdm(convert_iter, total=len(data_list)): 110 | if data_path is not None: 111 | if data_class<0: # abc or deepcad 112 | loaded_data.append(data_path) 113 | else: # furniture 114 | loaded_data.append((data_path,data_class)) 115 | 116 | print(f'Processed {len(loaded_data)}/{len(data_list)}') 117 | return loaded_data 118 | 119 | 120 | class SurfData(torch.utils.data.Dataset): 121 | """ Surface VAE Dataloader """ 122 | def __init__(self, input_data, input_list, validate=False, aug=False): 123 | self.validate = validate 124 | self.aug = aug 125 | 126 | # Load validation data 127 | if self.validate: 128 | print('Loading validation data...') 129 | with open(input_list, "rb") as tf: 130 | data_list = pickle.load(tf)['val'] 131 | 132 | datas = [] 133 | for uid in data_list: 134 | try: 135 | path = os.path.join(input_data, str(math.floor(int(uid.split('.')[0])/10000)).zfill(4), uid) 136 | except Exception: 137 | path = os.path.join(input_data, uid) 138 | 139 | with open(path, "rb") as tf: 140 | data = pickle.load(tf) 141 | _, _, surf_uv, _, _, _, _, _, _, _, _, _ = data.values() 142 | datas.append(surf_uv) 143 | self.data = np.vstack(datas) 144 | 145 | # Load training data (deduplicated) 146 | else: 147 | print('Loading training data...') 148 | with open(input_list, "rb") as tf: 149 | self.data = pickle.load(tf) 150 | 151 | print(len(self.data)) 152 | return 153 | 154 | def __len__(self): 155 | return len(self.data) 156 | 157 | def __getitem__(self, index): 158 | surf_uv = self.data[index] 159 | if np.random.rand()>0.5 and self.aug: 160 | for axis in ['x', 'y', 'z']: 161 | angle = random.choice([90, 180, 270]) 162 | surf_uv = rotate_point_cloud(surf_uv.reshape(-1, 3), angle, axis).reshape(32, 32, 3) 163 | return torch.FloatTensor(surf_uv) 164 | 165 | 166 | class EdgeData(torch.utils.data.Dataset): 167 | """ Edge VAE Dataloader """ 168 | def __init__(self, input_data, input_list, validate=False, aug=False): 169 | self.validate = validate 170 | self.aug = aug 171 | 172 | # Load validation data 173 | if self.validate: 174 | print('Loading validation data...') 175 | with open(input_list, "rb") as tf: 176 | data_list = pickle.load(tf)['val'] 177 | 178 | datas = [] 179 | for uid in tqdm(data_list): 180 | try: 181 | path = os.path.join(input_data, str(math.floor(int(uid.split('.')[0])/10000)).zfill(4), uid) 182 | except Exception: 183 | path = os.path.join(input_data, uid) 184 | 185 | with open(path, "rb") as tf: 186 | data = pickle.load(tf) 187 | 188 | _, _, _, edge_u, _, _, _, _, _, _, _, _ = data.values() 189 | datas.append(edge_u) 190 | self.data = np.vstack(datas) 191 | 192 | # Load training data (deduplicated) 193 | else: 194 | print('Loading training data...') 195 | with open(input_list, "rb") as tf: 196 | self.data = pickle.load(tf) 197 | 198 | print(len(self.data)) 199 | return 200 | 201 | def __len__(self): 202 | return len(self.data) 203 | 204 | def __getitem__(self, index): 205 | edge_u = self.data[index] 206 | # Data augmentation, randomly rotate 50% of the times 207 | if np.random.rand()>0.5 and self.aug: 208 | for axis in ['x', 'y', 'z']: 209 | angle = random.choice([90, 180, 270]) 210 | edge_u = rotate_point_cloud(edge_u, angle, axis) 211 | return torch.FloatTensor(edge_u) 212 | 213 | 214 | class SurfPosData(torch.utils.data.Dataset): 215 | """ Surface position (3D bbox) Dataloader """ 216 | def __init__(self, input_data, input_list, validate=False, aug=False, args=None): 217 | self.max_face = args.max_face 218 | self.max_edge = args.max_edge 219 | self.bbox_scaled = args.bbox_scaled 220 | self.aug = aug 221 | # Load data 222 | self.data = load_data(input_data, input_list, validate, args) 223 | # Inflate furniture x50 times for training 224 | if len(self.data)<2000 and not validate: 225 | self.data = self.data*50 226 | return 227 | 228 | def __len__(self): 229 | return len(self.data) 230 | 231 | def __getitem__(self, index): 232 | # Load data 233 | data_class = None 234 | if isinstance(self.data[index], tuple): 235 | data_path, data_class = self.data[index] 236 | else: 237 | data_path = self.data[index] 238 | 239 | with open(data_path, "rb") as tf: 240 | data = pickle.load(tf) 241 | _, _, _, _, _, _, _, _, surf_pos, _, _, _ = data.values() 242 | 243 | # Data augmentation 244 | random_num = np.random.rand() 245 | 246 | if random_num>0.5 and self.aug: 247 | # Get all eight corners 248 | surfpos_corners = bbox_corners(surf_pos) 249 | 250 | # Random rotation 251 | for axis in ['x', 'y', 'z']: 252 | angle = random.choice([90, 180, 270]) 253 | surfpos_corners = rotate_axis(surfpos_corners, angle, axis, normalized=True) 254 | 255 | # Re-compute the bottom left and top right corners 256 | surf_pos = get_bbox(surfpos_corners) 257 | surf_pos = surf_pos.reshape(len(surf_pos),6) 258 | 259 | # Make bbox range larger 260 | surf_pos = surf_pos * self.bbox_scaled 261 | 262 | # Randomly shuffle the sequence 263 | random_indices = np.random.permutation(surf_pos.shape[0]) 264 | surf_pos = surf_pos[random_indices] 265 | 266 | # Padding 267 | surf_pos = pad_repeat(surf_pos, self.max_face) 268 | 269 | # Randomly shuffle the sequence 270 | random_indices = np.random.permutation(surf_pos.shape[0]) 271 | surf_pos = surf_pos[random_indices] 272 | 273 | if data_class is not None: 274 | return ( 275 | torch.FloatTensor(surf_pos), 276 | torch.LongTensor([data_class+1]) # add 1, class 0 = uncond (furniture) 277 | ) 278 | else: 279 | return torch.FloatTensor(surf_pos) # abc or deepcad 280 | 281 | 282 | class SurfZData(torch.utils.data.Dataset): 283 | """ Surface latent geometry Dataloader """ 284 | def __init__(self, input_data, input_list, validate=False, aug=False, args=None): 285 | self.max_face = args.max_face 286 | self.max_edge = args.max_edge 287 | self.bbox_scaled = args.bbox_scaled 288 | self.aug = aug 289 | # Load data 290 | self.data = load_data(input_data, input_list, validate, args) 291 | # Inflate furniture x50 times for training 292 | if len(self.data)<2000 and not validate: 293 | self.data = self.data*50 294 | return 295 | 296 | def __len__(self): 297 | return len(self.data) 298 | 299 | def __getitem__(self, index): 300 | # Load data 301 | data_class = None 302 | if isinstance(self.data[index], tuple): 303 | data_path, data_class = self.data[index] 304 | else: 305 | data_path = self.data[index] 306 | 307 | with open(data_path, "rb") as tf: 308 | data = pickle.load(tf) 309 | _, _, surf_ncs, _, _, _, _, _, surf_pos, _, _, _ = data.values() 310 | 311 | # Data augmentation 312 | random_num = np.random.rand() 313 | 314 | if random_num>0.5 and self.aug: 315 | # Get all eight corners 316 | surfpos_corners = bbox_corners(surf_pos) 317 | 318 | # Random rotation 319 | for axis in ['x', 'y', 'z']: 320 | angle = random.choice([90, 180, 270]) 321 | surfpos_corners = rotate_axis(surfpos_corners, angle, axis, normalized=True) 322 | surf_ncs = rotate_axis(surf_ncs, angle, axis, normalized=False) 323 | 324 | # Re-compute the bottom left and top right corners 325 | surf_pos = get_bbox(surfpos_corners) 326 | surf_pos = surf_pos.reshape(len(surf_pos),6) 327 | 328 | # Make bbox range larger 329 | surf_pos = surf_pos * self.bbox_scaled 330 | 331 | # Randomly shuffle the sequence 332 | random_indices = np.random.permutation(surf_pos.shape[0]) 333 | surf_pos = surf_pos[random_indices] 334 | surf_ncs = surf_ncs[random_indices] 335 | 336 | # Pad data 337 | surf_pos, surf_mask = pad_zero(surf_pos, self.max_face, return_mask=True) 338 | surf_ncs = pad_zero(surf_ncs, self.max_face) 339 | 340 | if data_class is not None: 341 | return ( 342 | torch.FloatTensor(surf_pos), 343 | torch.FloatTensor(surf_ncs), 344 | torch.BoolTensor(surf_mask), 345 | torch.LongTensor([data_class+1]) # add 1, class 0 = uncond (furniture) 346 | ) 347 | else: 348 | return ( 349 | torch.FloatTensor(surf_pos), 350 | torch.FloatTensor(surf_ncs), 351 | torch.BoolTensor(surf_mask), 352 | ) # abc or deepcad 353 | 354 | 355 | class EdgePosData(torch.utils.data.Dataset): 356 | """ Edge Position (3D bbox) Dataloader """ 357 | def __init__(self, input_data, input_list, validate=False, aug=False, args=None): 358 | self.max_face = args.max_face 359 | self.max_edge = args.max_edge 360 | self.bbox_scaled = args.bbox_scaled 361 | self.aug = aug 362 | self.data = [] 363 | # Load data 364 | self.data = load_data(input_data, input_list, validate, args) 365 | # Inflate furniture x50 times for training 366 | if len(self.data)<2000 and not validate: 367 | self.data = self.data*50 368 | return 369 | 370 | def __len__(self): 371 | return len(self.data) 372 | 373 | def __getitem__(self, index): 374 | # Load data 375 | data_class = None 376 | if isinstance(self.data[index], tuple): 377 | data_path, data_class = self.data[index] 378 | else: 379 | data_path = self.data[index] 380 | 381 | with open(data_path, "rb") as tf: 382 | data = pickle.load(tf) 383 | 384 | _, _, surf_ncs, _, _, _, _, faceEdge_adj, surf_pos, edge_pos, _, _ = data.values() 385 | 386 | # Data augmentation 387 | random_num = np.random.rand() 388 | if random_num > 0.5 and self.aug: 389 | # Get all eight corners 390 | surfpos_corners = bbox_corners(surf_pos) 391 | edgepos_corners = bbox_corners(edge_pos) 392 | 393 | # Random rotation 394 | for axis in ['x', 'y', 'z']: 395 | angle = random.choice([90, 180, 270]) 396 | surfpos_corners = rotate_axis(surfpos_corners, angle, axis, normalized=True) 397 | edgepos_corners = rotate_axis(edgepos_corners, angle, axis, normalized=True) 398 | surf_ncs = rotate_axis(surf_ncs, angle, axis, normalized=False) 399 | 400 | # Re-compute the bottom left and top right corners 401 | surf_pos = get_bbox(surfpos_corners) 402 | surf_pos = surf_pos.reshape(len(surf_pos),6) 403 | edge_pos = get_bbox(edgepos_corners) 404 | edge_pos = edge_pos.reshape(len(edge_pos),6) 405 | 406 | # Increase bbox value range 407 | surf_pos = surf_pos * self.bbox_scaled 408 | edge_pos = edge_pos * self.bbox_scaled 409 | 410 | # Mating duplication 411 | edge_pos_duplicated = [] 412 | for adj in faceEdge_adj: 413 | edge_pos_duplicated.append(edge_pos[adj]) 414 | 415 | # Randomly shuffle the edges per face 416 | edge_pos_new = [] 417 | for pos in edge_pos_duplicated: 418 | random_indices = np.random.permutation(pos.shape[0]) 419 | pos = pos[random_indices] 420 | pos = pad_repeat(pos, self.max_edge) #make sure some values are always repeated 421 | random_indices = np.random.permutation(pos.shape[0]) 422 | pos = pos[random_indices] 423 | edge_pos_new.append(pos) 424 | edge_pos = np.stack(edge_pos_new) 425 | 426 | # Randomly shuffle the face sequence 427 | random_indices = np.random.permutation(surf_pos.shape[0]) 428 | surf_pos = surf_pos[random_indices] 429 | edge_pos = edge_pos[random_indices] 430 | surf_ncs = surf_ncs[random_indices] 431 | 432 | # Padding 433 | surf_pos, surf_mask = pad_zero(surf_pos, self.max_face, return_mask=True) 434 | surf_ncs = pad_zero(surf_ncs, self.max_face) 435 | edge_pos = pad_zero(edge_pos, self.max_face) 436 | 437 | if data_class is not None: 438 | return ( 439 | torch.FloatTensor(edge_pos), 440 | torch.FloatTensor(surf_ncs), 441 | torch.FloatTensor(surf_pos), 442 | torch.BoolTensor(surf_mask), 443 | torch.LongTensor([data_class+1]) # add 1, class 0 = uncond (furniture) 444 | ) 445 | else: 446 | return ( 447 | torch.FloatTensor(edge_pos), 448 | torch.FloatTensor(surf_ncs), 449 | torch.FloatTensor(surf_pos), 450 | torch.BoolTensor(surf_mask), 451 | )# abc or deepcad 452 | 453 | 454 | 455 | class EdgeZData(torch.utils.data.Dataset): 456 | """ Edge Latent z Dataloader """ 457 | def __init__(self, input_data, input_list, validate=False, aug=False, args=None): 458 | self.max_face = args.max_face 459 | self.max_edge = args.max_edge 460 | self.bbox_scaled = args.bbox_scaled 461 | self.aug = aug 462 | self.data = [] 463 | # Load data 464 | self.data = load_data(input_data, input_list, validate, args) 465 | # Inflate furniture x50 times for training 466 | if len(self.data)<2000 and not validate: 467 | self.data = self.data*50 468 | return 469 | 470 | def __len__(self): 471 | return len(self.data) 472 | 473 | def __getitem__(self, index): 474 | # Load data 475 | data_class = None 476 | if isinstance(self.data[index], tuple): 477 | data_path, data_class = self.data[index] 478 | else: 479 | data_path = self.data[index] 480 | 481 | with open(data_path, "rb") as tf: 482 | data = pickle.load(tf) 483 | 484 | _, _, surf_ncs, edge_ncs, corner_wcs, _, _, faceEdge_adj, surf_pos, edge_pos, _, _ = data.values() 485 | 486 | # Data augmentation 487 | random_num = np.random.rand() 488 | if random_num > 0.5 and self.aug: 489 | # Get all eight corners 490 | surfpos_corners = bbox_corners(surf_pos) 491 | edgepos_corners = bbox_corners(edge_pos) 492 | 493 | # Random rotation 494 | for axis in ['x', 'y', 'z']: 495 | angle = random.choice([90, 180, 270]) 496 | surfpos_corners = rotate_axis(surfpos_corners, angle, axis, normalized=True) 497 | edgepos_corners = rotate_axis(edgepos_corners, angle, axis, normalized=True) 498 | corner_wcs = rotate_axis(corner_wcs, angle, axis, normalized=True) 499 | surf_ncs = rotate_axis(surf_ncs, angle, axis, normalized=False) 500 | edge_ncs = rotate_axis(edge_ncs, angle, axis, normalized=False) 501 | 502 | # Re-compute the bottom left and top right corners 503 | surf_pos = get_bbox(surfpos_corners) 504 | surf_pos = surf_pos.reshape(len(surf_pos),6) 505 | edge_pos = get_bbox(edgepos_corners) 506 | edge_pos = edge_pos.reshape(len(edge_pos),6) 507 | 508 | # Increase value range 509 | surf_pos = surf_pos * self.bbox_scaled 510 | edge_pos = edge_pos * self.bbox_scaled 511 | corner_wcs = corner_wcs * self.bbox_scaled 512 | 513 | # Mating duplication 514 | edge_pos_duplicated = [] 515 | vertex_pos_duplicated = [] 516 | edge_ncs_duplicated = [] 517 | for adj in faceEdge_adj: 518 | edge_ncs_duplicated.append(edge_ncs[adj]) 519 | edge_pos_duplicated.append(edge_pos[adj]) 520 | corners = corner_wcs[adj] 521 | corners_sorted = [] 522 | for corner in corners: 523 | sorted_indices = np.lexsort((corner[:, 2], corner[:, 1], corner[:, 0])) 524 | corners_sorted.append(corner[sorted_indices].flatten()) # 1 x 6 corner pos 525 | corners = np.stack(corners_sorted) 526 | vertex_pos_duplicated.append(corners) 527 | 528 | # Edge Shuffle and Padding 529 | edge_pos_new = [] 530 | edge_ncs_new = [] 531 | vert_pos_new = [] 532 | edge_mask = [] 533 | for pos, ncs, vert in zip(edge_pos_duplicated, edge_ncs_duplicated, vertex_pos_duplicated): 534 | random_indices = np.random.permutation(pos.shape[0]) 535 | pos = pos[random_indices] 536 | ncs = ncs[random_indices] 537 | vert = vert[random_indices] 538 | 539 | pos, mask = pad_zero(pos, self.max_edge, return_mask=True) 540 | ncs = pad_zero(ncs, self.max_edge) 541 | vert = pad_zero(vert, self.max_edge) 542 | 543 | edge_pos_new.append(pos) 544 | edge_ncs_new.append(ncs) 545 | edge_mask.append(mask) 546 | vert_pos_new.append(vert) 547 | 548 | edge_pos = np.stack(edge_pos_new) 549 | edge_ncs = np.stack(edge_ncs_new) 550 | edge_mask = np.stack(edge_mask) 551 | vertex_pos = np.stack(vert_pos_new) 552 | 553 | # Face Shuffle and Padding 554 | random_indices = np.random.permutation(surf_pos.shape[0]) 555 | surf_pos = surf_pos[random_indices] 556 | edge_pos = edge_pos[random_indices] 557 | surf_ncs = surf_ncs[random_indices] 558 | edge_ncs = edge_ncs[random_indices] 559 | edge_mask = edge_mask[random_indices] 560 | vertex_pos = vertex_pos[random_indices] 561 | 562 | # Padding 563 | surf_pos = pad_zero(surf_pos, self.max_face) 564 | surf_ncs = pad_zero(surf_ncs, self.max_face) 565 | edge_pos = pad_zero(edge_pos, self.max_face) 566 | edge_ncs = pad_zero(edge_ncs, self.max_face) 567 | vertex_pos = pad_zero(vertex_pos, self.max_face) 568 | padding = np.zeros((self.max_face-len(edge_mask), *edge_mask.shape[1:]))==0 569 | edge_mask = np.concatenate([edge_mask, padding], 0) 570 | 571 | if data_class is not None: 572 | return ( 573 | torch.FloatTensor(edge_ncs), 574 | torch.FloatTensor(edge_pos), 575 | torch.BoolTensor(edge_mask), 576 | torch.FloatTensor(surf_ncs), 577 | torch.FloatTensor(surf_pos), 578 | torch.FloatTensor(vertex_pos), 579 | torch.LongTensor([data_class+1]) # add 1, class 0 = uncond (furniture) 580 | ) 581 | else: 582 | return ( 583 | torch.FloatTensor(edge_ncs), 584 | torch.FloatTensor(edge_pos), 585 | torch.BoolTensor(edge_mask), 586 | torch.FloatTensor(surf_ncs), 587 | torch.FloatTensor(surf_pos), 588 | torch.FloatTensor(vertex_pos), # uncond deepcad/abc 589 | ) 590 | 591 | -------------------------------------------------------------------------------- /LICENSE_GPL: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import math 3 | import torch 4 | import torch.nn as nn 5 | import random 6 | import string 7 | import argparse 8 | from chamferdist import ChamferDistance 9 | from mpl_toolkits.mplot3d.art3d import Poly3DCollection 10 | from typing import List, Optional, Tuple, Union 11 | 12 | from OCC.Core.gp import gp_Pnt, gp_Pnt 13 | from OCC.Core.TColgp import TColgp_Array2OfPnt 14 | from OCC.Core.GeomAPI import GeomAPI_PointsToBSplineSurface, GeomAPI_PointsToBSpline 15 | from OCC.Core.GeomAbs import GeomAbs_C2 16 | from OCC.Core.BRepBuilderAPI import BRepBuilderAPI_MakeWire, BRepBuilderAPI_MakeFace, BRepBuilderAPI_MakeEdge 17 | from OCC.Extend.TopologyUtils import TopologyExplorer, WireExplorer 18 | from OCC.Core.TColgp import TColgp_Array1OfPnt 19 | from OCC.Core.gp import gp_Pnt 20 | from OCC.Core.ShapeFix import ShapeFix_Face, ShapeFix_Wire, ShapeFix_Edge 21 | from OCC.Core.ShapeAnalysis import ShapeAnalysis_Wire 22 | from OCC.Core.BRepBuilderAPI import BRepBuilderAPI_Sewing, BRepBuilderAPI_MakeSolid 23 | 24 | 25 | def generate_random_string(length): 26 | characters = string.ascii_letters + string.digits # You can include other characters if needed 27 | random_string = ''.join(random.choice(characters) for _ in range(length)) 28 | return random_string 29 | 30 | 31 | def get_bbox_norm(point_cloud): 32 | # Find the minimum and maximum coordinates along each axis 33 | min_x = np.min(point_cloud[:, 0]) 34 | max_x = np.max(point_cloud[:, 0]) 35 | 36 | min_y = np.min(point_cloud[:, 1]) 37 | max_y = np.max(point_cloud[:, 1]) 38 | 39 | min_z = np.min(point_cloud[:, 2]) 40 | max_z = np.max(point_cloud[:, 2]) 41 | 42 | # Create the 3D bounding box using the min and max values 43 | min_point = np.array([min_x, min_y, min_z]) 44 | max_point = np.array([max_x, max_y, max_z]) 45 | return np.linalg.norm(max_point - min_point) 46 | 47 | 48 | def compute_bbox_center_and_size(min_corner, max_corner): 49 | # Calculate the center 50 | center_x = (min_corner[0] + max_corner[0]) / 2 51 | center_y = (min_corner[1] + max_corner[1]) / 2 52 | center_z = (min_corner[2] + max_corner[2]) / 2 53 | center = np.array([center_x, center_y, center_z]) 54 | # Calculate the size 55 | size_x = max_corner[0] - min_corner[0] 56 | size_y = max_corner[1] - min_corner[1] 57 | size_z = max_corner[2] - min_corner[2] 58 | size = max(size_x, size_y, size_z) 59 | return center, size 60 | 61 | 62 | def randn_tensor( 63 | shape: Union[Tuple, List], 64 | generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None, 65 | device: Optional["torch.device"] = None, 66 | dtype: Optional["torch.dtype"] = None, 67 | layout: Optional["torch.layout"] = None, 68 | ): 69 | """This is a helper function that allows to create random tensors on the desired `device` with the desired `dtype`. When 70 | passing a list of generators one can seed each batched size individually. If CPU generators are passed the tensor 71 | will always be created on CPU. 72 | """ 73 | # device on which tensor is created defaults to device 74 | rand_device = device 75 | batch_size = shape[0] 76 | 77 | layout = layout or torch.strided 78 | device = device or torch.device("cpu") 79 | 80 | if generator is not None: 81 | gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type 82 | if gen_device_type != device.type and gen_device_type == "cpu": 83 | rand_device = "cpu" 84 | elif gen_device_type != device.type and gen_device_type == "cuda": 85 | raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}.") 86 | 87 | if isinstance(generator, list): 88 | shape = (1,) + shape[1:] 89 | latents = [ 90 | torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout) 91 | for i in range(batch_size) 92 | ] 93 | latents = torch.cat(latents, dim=0).to(device) 94 | else: 95 | latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device) 96 | 97 | return latents 98 | 99 | 100 | def pad_repeat(x, max_len): 101 | repeat_times = math.floor(max_len/len(x)) 102 | sep = max_len-repeat_times*len(x) 103 | sep1 = np.repeat(x[:sep], repeat_times+1, axis=0) 104 | sep2 = np.repeat(x[sep:], repeat_times, axis=0) 105 | x_repeat = np.concatenate([sep1, sep2], 0) 106 | return x_repeat 107 | 108 | 109 | def pad_zero(x, max_len, return_mask=False): 110 | keys = np.ones(len(x)) 111 | padding = np.zeros((max_len-len(x))).astype(int) 112 | mask = 1-np.concatenate([keys, padding]) == 1 113 | padding = np.zeros((max_len-len(x), *x.shape[1:])) 114 | x_padded = np.concatenate([x, padding], axis=0) 115 | if return_mask: 116 | return x_padded, mask 117 | else: 118 | return x_padded 119 | 120 | 121 | def plot_3d_bbox(ax, min_corner, max_corner, color='r'): 122 | """ 123 | Helper function for plotting 3D bounding boxese 124 | """ 125 | vertices = [ 126 | (min_corner[0], min_corner[1], min_corner[2]), 127 | (max_corner[0], min_corner[1], min_corner[2]), 128 | (max_corner[0], max_corner[1], min_corner[2]), 129 | (min_corner[0], max_corner[1], min_corner[2]), 130 | (min_corner[0], min_corner[1], max_corner[2]), 131 | (max_corner[0], min_corner[1], max_corner[2]), 132 | (max_corner[0], max_corner[1], max_corner[2]), 133 | (min_corner[0], max_corner[1], max_corner[2]) 134 | ] 135 | # Define the 12 triangles composing the box 136 | faces = [ 137 | [vertices[0], vertices[1], vertices[2], vertices[3]], 138 | [vertices[4], vertices[5], vertices[6], vertices[7]], 139 | [vertices[0], vertices[1], vertices[5], vertices[4]], 140 | [vertices[2], vertices[3], vertices[7], vertices[6]], 141 | [vertices[1], vertices[2], vertices[6], vertices[5]], 142 | [vertices[4], vertices[7], vertices[3], vertices[0]] 143 | ] 144 | ax.add_collection3d(Poly3DCollection(faces, facecolors='blue', linewidths=1, edgecolors=color, alpha=0)) 145 | return 146 | 147 | 148 | def get_args_vae(): 149 | parser = argparse.ArgumentParser() 150 | parser.add_argument('--data', type=str, default='data_process/deepcad_parsed', 151 | help='Path to data folder') 152 | parser.add_argument('--train_list', type=str, default='data_process/deepcad_data_split_6bit_surface.pkl', 153 | help='Path to training list') 154 | parser.add_argument('--val_list', type=str, default='data_process/deepcad_data_split_6bit.pkl', 155 | help='Path to validation list') 156 | # Training parameters 157 | parser.add_argument("--option", type=str, choices=['surface', 'edge'], default='surface', 158 | help="Choose between option surface or edge (default: surface)") 159 | parser.add_argument('--batch_size', type=int, default=512, help='input batch size') 160 | parser.add_argument('--train_nepoch', type=int, default=200, help='number of epochs to train for') 161 | parser.add_argument('--save_nepoch', type=int, default=20, help='number of epochs to save model') 162 | parser.add_argument('--test_nepoch', type=int, default=10, help='number of epochs to test model') 163 | parser.add_argument("--data_aug", action='store_true', help='Use data augmentation') 164 | parser.add_argument("--finetune", action='store_true', help='Finetune from existing weights') 165 | parser.add_argument("--weight", type=str, default=None, help='Weight path when finetuning') 166 | parser.add_argument("--gpu", type=int, nargs='+', default=[0], help="GPU IDs to use for training (default: [0])") 167 | # Save dirs and reload 168 | parser.add_argument('--env', type=str, default="surface_vae", help='environment') 169 | parser.add_argument('--dir_name', type=str, default="proj_log", help='name of the log folder.') 170 | args = parser.parse_args() 171 | # saved folder 172 | args.save_dir = f'{args.dir_name}/{args.env}' 173 | return args 174 | 175 | 176 | def get_args_ldm(): 177 | parser = argparse.ArgumentParser() 178 | parser.add_argument('--data', type=str, default='data_process/deepcad_parsed', 179 | help='Path to data folder') 180 | parser.add_argument('--list', type=str, default='data_process/deepcad_data_split_6bit.pkl', 181 | help='Path to data list') 182 | parser.add_argument('--surfvae', type=str, default='proj_log/deepcad_surfvae/epoch_400.pt', 183 | help='Path to pretrained surface vae weights') 184 | parser.add_argument('--edgevae', type=str, default='proj_log/deepcad_edgevae/epoch_300.pt', 185 | help='Path to pretrained edge vae weights') 186 | parser.add_argument("--option", type=str, choices=['surfpos', 'surfz', 'edgepos', 'edgez'], default='surfpos', 187 | help="Choose between option [surfpos,edgepos,surfz,edgez] (default: surfpos)") 188 | # Training parameters 189 | parser.add_argument('--batch_size', type=int, default=512, help='input batch size') 190 | parser.add_argument('--train_nepoch', type=int, default=3000, help='number of epochs to train for') 191 | parser.add_argument('--test_nepoch', type=int, default=25, help='number of epochs to test model') 192 | parser.add_argument('--save_nepoch', type=int, default=50, help='number of epochs to save model') 193 | parser.add_argument('--max_face', type=int, default=50, help='maximum number of faces') 194 | parser.add_argument('--max_edge', type=int, default=30, help='maximum number of edges per face') 195 | parser.add_argument('--threshold', type=float, default=0.05, help='minimum threshold between two faces') 196 | parser.add_argument('--bbox_scaled', type=float, default=3, help='scaled the bbox') 197 | parser.add_argument('--z_scaled', type=float, default=1, help='scaled the latent z') 198 | parser.add_argument("--gpu", type=int, nargs='+', default=[0, 1], help="GPU IDs to use for training (default: [0, 1])") 199 | parser.add_argument("--data_aug", action='store_true', help='Use data augmentation') 200 | parser.add_argument("--cf", action='store_true', help='Use data augmentation') 201 | # Save dirs and reload 202 | parser.add_argument('--env', type=str, default="surface_pos", help='environment') 203 | parser.add_argument('--dir_name', type=str, default="proj_log", help='name of the log folder.') 204 | args = parser.parse_args() 205 | # saved folder 206 | args.save_dir = f'{args.dir_name}/{args.env}' 207 | return args 208 | 209 | 210 | def rotate_point_cloud(point_cloud, angle_degrees, axis): 211 | """ 212 | Rotate a point cloud around its center by a specified angle in degrees along a specified axis. 213 | 214 | Args: 215 | - point_cloud: Numpy array of shape (N, 3) representing the point cloud. 216 | - angle_degrees: Angle of rotation in degrees. 217 | - axis: Axis of rotation. Can be 'x', 'y', or 'z'. 218 | 219 | Returns: 220 | - rotated_point_cloud: Numpy array of shape (N, 3) representing the rotated point cloud. 221 | """ 222 | 223 | # Convert angle to radians 224 | angle_radians = np.radians(angle_degrees) 225 | 226 | # Compute rotation matrix based on the specified axis 227 | if axis == 'x': 228 | rotation_matrix = np.array([[1, 0, 0], 229 | [0, np.cos(angle_radians), -np.sin(angle_radians)], 230 | [0, np.sin(angle_radians), np.cos(angle_radians)]]) 231 | elif axis == 'y': 232 | rotation_matrix = np.array([[np.cos(angle_radians), 0, np.sin(angle_radians)], 233 | [0, 1, 0], 234 | [-np.sin(angle_radians), 0, np.cos(angle_radians)]]) 235 | elif axis == 'z': 236 | rotation_matrix = np.array([[np.cos(angle_radians), -np.sin(angle_radians), 0], 237 | [np.sin(angle_radians), np.cos(angle_radians), 0], 238 | [0, 0, 1]]) 239 | else: 240 | raise ValueError("Invalid axis. Must be 'x', 'y', or 'z'.") 241 | 242 | # Center the point cloud 243 | center = np.mean(point_cloud, axis=0) 244 | centered_point_cloud = point_cloud - center 245 | 246 | # Apply rotation 247 | rotated_point_cloud = np.dot(centered_point_cloud, rotation_matrix.T) 248 | 249 | # Translate back to original position 250 | rotated_point_cloud += center 251 | 252 | # Find the maximum absolute coordinate value 253 | max_abs_coord = np.max(np.abs(rotated_point_cloud)) 254 | 255 | # Scale the point cloud to fit within the -1 to 1 cube 256 | normalized_point_cloud = rotated_point_cloud / max_abs_coord 257 | 258 | return normalized_point_cloud 259 | 260 | 261 | def get_bbox(pnts): 262 | """ 263 | Get the tighest fitting 3D (axis-aligned) bounding box giving a set of points 264 | """ 265 | bbox_corners = [] 266 | for point_cloud in pnts: 267 | # Find the minimum and maximum coordinates along each axis 268 | min_x = np.min(point_cloud[:, 0]) 269 | max_x = np.max(point_cloud[:, 0]) 270 | 271 | min_y = np.min(point_cloud[:, 1]) 272 | max_y = np.max(point_cloud[:, 1]) 273 | 274 | min_z = np.min(point_cloud[:, 2]) 275 | max_z = np.max(point_cloud[:, 2]) 276 | 277 | # Create the 3D bounding box using the min and max values 278 | min_point = np.array([min_x, min_y, min_z]) 279 | max_point = np.array([max_x, max_y, max_z]) 280 | bbox_corners.append([min_point, max_point]) 281 | return np.array(bbox_corners) 282 | 283 | 284 | def bbox_corners(bboxes): 285 | """ 286 | Given the bottom-left and top-right corners of the bbox 287 | Return all eight corners 288 | """ 289 | bboxes_all_corners = [] 290 | for bbox in bboxes: 291 | bottom_left, top_right = bbox[:3], bbox[3:] 292 | # Bottom 4 corners 293 | bottom_front_left = bottom_left 294 | bottom_front_right = (top_right[0], bottom_left[1], bottom_left[2]) 295 | bottom_back_left = (bottom_left[0], top_right[1], bottom_left[2]) 296 | bottom_back_right = (top_right[0], top_right[1], bottom_left[2]) 297 | 298 | # Top 4 corners 299 | top_front_left = (bottom_left[0], bottom_left[1], top_right[2]) 300 | top_front_right = (top_right[0], bottom_left[1], top_right[2]) 301 | top_back_left = (bottom_left[0], top_right[1], top_right[2]) 302 | top_back_right = top_right 303 | 304 | # Combine all coordinates 305 | all_corners = [ 306 | bottom_front_left, 307 | bottom_front_right, 308 | bottom_back_left, 309 | bottom_back_right, 310 | top_front_left, 311 | top_front_right, 312 | top_back_left, 313 | top_back_right, 314 | ] 315 | bboxes_all_corners.append(np.vstack(all_corners)) 316 | bboxes_all_corners = np.array(bboxes_all_corners) 317 | return bboxes_all_corners 318 | 319 | 320 | def rotate_axis(pnts, angle_degrees, axis, normalized=False): 321 | """ 322 | Rotate a point cloud around its center by a specified angle in degrees along a specified axis. 323 | 324 | Args: 325 | - point_cloud: Numpy array of shape (N, ..., 3) representing the point cloud. 326 | - angle_degrees: Angle of rotation in degrees. 327 | - axis: Axis of rotation. Can be 'x', 'y', or 'z'. 328 | 329 | Returns: 330 | - rotated_point_cloud: Numpy array of shape (N, 3) representing the rotated point cloud. 331 | """ 332 | 333 | # Convert angle to radians 334 | angle_radians = np.radians(angle_degrees) 335 | 336 | # Convert points to homogeneous coordinates 337 | shape = list(np.shape(pnts)) 338 | shape[-1] = 1 339 | pnts_homogeneous = np.concatenate((pnts, np.ones(shape)), axis=-1) 340 | 341 | # Compute rotation matrix based on the specified axis 342 | if axis == 'x': 343 | rotation_matrix = np.array([ 344 | [1, 0, 0, 0], 345 | [0, np.cos(angle_radians), -np.sin(angle_radians), 0], 346 | [0, np.sin(angle_radians), np.cos(angle_radians), 0], 347 | [0, 0, 0, 1] 348 | ]) 349 | elif axis == 'y': 350 | rotation_matrix = np.array([ 351 | [np.cos(angle_radians), 0, np.sin(angle_radians), 0], 352 | [0, 1, 0, 0], 353 | [-np.sin(angle_radians), 0, np.cos(angle_radians), 0], 354 | [0, 0, 0, 1] 355 | ]) 356 | elif axis == 'z': 357 | rotation_matrix = np.array([ 358 | [np.cos(angle_radians), -np.sin(angle_radians), 0, 0], 359 | [np.sin(angle_radians), np.cos(angle_radians), 0, 0], 360 | [0, 0, 1, 0], 361 | [0, 0, 0, 1] 362 | ]) 363 | else: 364 | raise ValueError("Invalid axis. Must be 'x', 'y', or 'z'.") 365 | 366 | # Apply rotation 367 | rotated_pnts_homogeneous = np.dot(pnts_homogeneous, rotation_matrix.T) 368 | rotated_pnts = rotated_pnts_homogeneous[...,:3] 369 | 370 | # Scale the point cloud to fit within the -1 to 1 cube 371 | if normalized: 372 | max_abs_coord = np.max(np.abs(rotated_pnts)) 373 | rotated_pnts = rotated_pnts / max_abs_coord 374 | 375 | return rotated_pnts 376 | 377 | 378 | def rescale_bbox(bboxes, scale): 379 | # Apply scaling factors to bounding boxes 380 | scaled_bboxes = bboxes*scale 381 | return scaled_bboxes 382 | 383 | 384 | def translate_bbox(bboxes): 385 | """ 386 | Randomly move object within the cube (x,y,z direction) 387 | """ 388 | point_cloud = bboxes.reshape(-1,3) 389 | min_x = np.min(point_cloud[:, 0]) 390 | max_x = np.max(point_cloud[:, 0]) 391 | min_y = np.min(point_cloud[:, 1]) 392 | max_y = np.max(point_cloud[:, 1]) 393 | min_z = np.min(point_cloud[:, 2]) 394 | max_z = np.max(point_cloud[:, 2]) 395 | x_offset = np.random.uniform( np.min(-1-min_x,0), np.max(1-max_x,0) ) 396 | y_offset = np.random.uniform( np.min(-1-min_y,0), np.max(1-max_y,0) ) 397 | z_offset = np.random.uniform( np.min(-1-min_z,0), np.max(1-max_z,0) ) 398 | random_translation = np.array([x_offset,y_offset,z_offset]) 399 | bboxes_translated = bboxes + random_translation 400 | return bboxes_translated 401 | 402 | 403 | def edge2loop(face_edges): 404 | face_edges_flatten = face_edges.reshape(-1,3) 405 | # connect end points by closest distance 406 | merged_vertex_id = [] 407 | for edge_idx, startend in enumerate(face_edges): 408 | self_id = [2*edge_idx, 2*edge_idx+1] 409 | # left endpoint 410 | distance = np.linalg.norm(face_edges_flatten - startend[0], axis=1) 411 | min_id = list(np.argsort(distance)) 412 | min_id_noself = [x for x in min_id if x not in self_id] 413 | merged_vertex_id.append(sorted([2*edge_idx, min_id_noself[0]])) 414 | # right endpoint 415 | distance = np.linalg.norm(face_edges_flatten - startend[1], axis=1) 416 | min_id = list(np.argsort(distance)) 417 | min_id_noself = [x for x in min_id if x not in self_id] 418 | merged_vertex_id.append(sorted([2*edge_idx+1, min_id_noself[0]])) 419 | 420 | merged_vertex_id = np.unique(np.array(merged_vertex_id),axis=0) 421 | return merged_vertex_id 422 | 423 | 424 | def keep_largelist(int_lists): 425 | # Initialize a list to store the largest integer lists 426 | largest_int_lists = [] 427 | 428 | # Convert each list to a set for efficient comparison 429 | sets = [set(lst) for lst in int_lists] 430 | 431 | # Iterate through the sets and check if they are subsets of others 432 | for i, s1 in enumerate(sets): 433 | is_subset = False 434 | for j, s2 in enumerate(sets): 435 | if i!=j and s1.issubset(s2) and s1 != s2: 436 | is_subset = True 437 | break 438 | if not is_subset: 439 | largest_int_lists.append(list(s1)) 440 | 441 | # Initialize a set to keep track of seen tuples 442 | seen_tuples = set() 443 | 444 | # Initialize a list to store unique integer lists 445 | unique_int_lists = [] 446 | 447 | # Iterate through the input list 448 | for int_list in largest_int_lists: 449 | # Convert the list to a tuple for hashing 450 | int_tuple = tuple(sorted(int_list)) 451 | 452 | # Check if the tuple is not in the set of seen tuples 453 | if int_tuple not in seen_tuples: 454 | # Add the tuple to the set of seen tuples 455 | seen_tuples.add(int_tuple) 456 | 457 | # Add the original list to the list of unique integer lists 458 | unique_int_lists.append(int_list) 459 | 460 | return unique_int_lists 461 | 462 | 463 | def detect_shared_vertex(edgeV_cad, edge_mask_cad, edgeV_bbox): 464 | """ 465 | Find the shared vertices 466 | """ 467 | edge_id_offset = 2 * np.concatenate([np.array([0]),np.cumsum((edge_mask_cad==False).sum(1))])[:-1] 468 | valid = True 469 | 470 | # Detect shared-vertex on seperate face loop 471 | used_vertex = [] 472 | face_sep_merges = [] 473 | for face_idx, (face_edges, face_edges_mask, bbox_edges) in enumerate(zip(edgeV_cad, edge_mask_cad, edgeV_bbox)): 474 | face_edges = face_edges[~face_edges_mask] 475 | face_edges = face_edges.reshape(len(face_edges),2,3) 476 | face_start_id = edge_id_offset[face_idx] 477 | 478 | # connect end points by closest distance (edge bbox) 479 | merged_vertex_id = edge2loop(bbox_edges) 480 | if len(merged_vertex_id) == len(face_edges): 481 | merged_vertex_id = face_start_id + merged_vertex_id 482 | face_sep_merges.append(merged_vertex_id) 483 | used_vertex.append(bbox_edges*3) 484 | print('[PASS]') 485 | continue 486 | 487 | # connect end points by closest distance (vertex pos) 488 | merged_vertex_id = edge2loop(face_edges) 489 | if len(merged_vertex_id) == len(face_edges): 490 | merged_vertex_id = face_start_id + merged_vertex_id 491 | face_sep_merges.append(merged_vertex_id) 492 | used_vertex.append(face_edges) 493 | print('[PASS]') 494 | continue 495 | 496 | print('[FAILED]') 497 | valid = False 498 | break 499 | 500 | # Invalid 501 | if not valid: 502 | assert False 503 | 504 | # Detect shared-vertex across faces 505 | total_pnts = np.vstack(used_vertex) 506 | total_pnts = total_pnts.reshape(len(total_pnts),2,3) 507 | total_pnts_flatten = total_pnts.reshape(-1,3) 508 | 509 | total_ids = [] 510 | for face_idx, face_merge in enumerate(face_sep_merges): 511 | # non-self merge centers 512 | nonself_face_idx = list(set(np.arange(len(face_sep_merges))) - set([face_idx])) 513 | nonself_face_merges = [face_sep_merges[x] for x in nonself_face_idx] 514 | nonself_face_merges = np.vstack(nonself_face_merges) 515 | nonself_merged_centers = total_pnts_flatten[nonself_face_merges].mean(1) 516 | 517 | # connect end points by closest distance 518 | across_merge_id = [] 519 | for merge_id in face_merge: 520 | merged_center = total_pnts_flatten[merge_id].mean(0) 521 | distance = np.linalg.norm(nonself_merged_centers - merged_center, axis=1) 522 | nonself_match_id = nonself_face_merges[np.argsort(distance)[0]] 523 | joint_merge_id = list(nonself_match_id) + list(merge_id) 524 | across_merge_id.append(joint_merge_id) 525 | total_ids += across_merge_id 526 | 527 | # Merge T-junctions 528 | while (True): 529 | no_merge = True 530 | final_merge_id = [] 531 | 532 | # iteratelly merge until no changes happen 533 | for i in range(len(total_ids)): 534 | perform_merge = False 535 | 536 | for j in range(i+1,len(total_ids)): 537 | # check if vertex can be further merged 538 | max_num = max(len(total_ids[i]), len(total_ids[j])) 539 | union = set(total_ids[i]).union(set(total_ids[j])) 540 | common = set(total_ids[i]).intersection(set(total_ids[j])) 541 | if len(union) > max_num and len(common)>0: 542 | final_merge_id.append(list(union)) 543 | perform_merge = True 544 | no_merge = False 545 | break 546 | 547 | if not perform_merge: 548 | final_merge_id.append(total_ids[i]) # no-merge 549 | 550 | total_ids = final_merge_id 551 | if no_merge: break 552 | 553 | # remove subsets 554 | total_ids = keep_largelist(total_ids) 555 | 556 | # merge again base on absolute coordinate value, required for >3 T-junction 557 | tobe_merged_centers = [total_pnts_flatten[x].mean(0) for x in total_ids] 558 | tobe_centers = np.array(tobe_merged_centers) 559 | distances = np.linalg.norm(tobe_centers[:, np.newaxis, :] - tobe_centers, axis=2) 560 | close_points = distances < 0.1 561 | mask = np.tril(np.ones_like(close_points, dtype=bool), k=-1) 562 | non_diagonal_indices = np.where(close_points & mask) 563 | row_indices, column_indices = non_diagonal_indices 564 | 565 | # update the total_ids 566 | total_ids_updated = [] 567 | for row, col in zip(row_indices, column_indices): 568 | total_ids_updated.append(total_ids[row] + total_ids[col]) 569 | for index, ids in enumerate(total_ids): 570 | if index not in list(row_indices) and index not in list(column_indices): 571 | total_ids_updated.append(ids) 572 | total_ids = total_ids_updated 573 | 574 | # merged vertices 575 | unique_vertices = [] 576 | for center_id in total_ids: 577 | center_pnts = total_pnts_flatten[center_id].mean(0) / 3.0 578 | unique_vertices.append(center_pnts) 579 | unique_vertices = np.vstack(unique_vertices) 580 | 581 | new_vertex_dict = {} 582 | for new_id, old_ids in enumerate(total_ids): 583 | new_vertex_dict[new_id] = old_ids 584 | 585 | return [unique_vertices, new_vertex_dict] 586 | 587 | 588 | def detect_shared_edge(unique_vertices, new_vertex_dict, edge_z_cad, surf_z_cad, z_threshold, edge_mask_cad): 589 | """ 590 | Find the shared edges 591 | """ 592 | init_edges = edge_z_cad 593 | 594 | # re-assign edge start/end to unique vertices 595 | new_ids = [] 596 | for old_id in np.arange(2*len(init_edges)): 597 | new_id = [] 598 | for key, value in new_vertex_dict.items(): 599 | # Check if the desired number is in the associated list 600 | if old_id in value: 601 | new_id.append(key) 602 | assert len(new_id) == 1 # should only return one unique value 603 | new_ids.append(new_id[0]) 604 | 605 | EdgeVertexAdj = np.array(new_ids).reshape(-1,2) 606 | 607 | # find edges assigned to the same start/end 608 | similar_edges = [] 609 | for i, s1 in enumerate(EdgeVertexAdj): 610 | for j, s2 in enumerate(EdgeVertexAdj): 611 | if i!=j and set(s1) == set(s2): # same start/end 612 | z1 = init_edges[i] 613 | z2 = init_edges[j] 614 | z_diff = np.abs(z1-z2).mean() 615 | if z_diff < z_threshold: # check z difference 616 | similar_edges.append(sorted([i,j])) 617 | # else: 618 | # print('z latent beyond...') 619 | similar_edges = np.unique(np.array(similar_edges),axis=0) 620 | 621 | # should reduce total edges by two 622 | if not 2*len(similar_edges) == len(EdgeVertexAdj): 623 | assert False, 'edge not reduced by 2' 624 | 625 | # unique edges 626 | unique_edge_id = similar_edges[:,0] 627 | EdgeVertexAdj = EdgeVertexAdj[unique_edge_id] 628 | unique_edges = init_edges[unique_edge_id] 629 | 630 | # unique faces 631 | unique_faces = surf_z_cad 632 | FaceEdgeAdj = [] 633 | ranges = np.concatenate([np.array([0]),np.cumsum((edge_mask_cad==False).sum(1))]) 634 | for index in range(len(ranges)-1): 635 | adj_ids = np.arange(ranges[index], ranges[index+1]) 636 | new_ids = [] 637 | for id in adj_ids: 638 | new_id = np.where(similar_edges == id)[0] 639 | assert len(new_id) == 1 640 | new_ids.append(new_id[0]) 641 | FaceEdgeAdj.append(new_ids) 642 | 643 | print(f'Post-process: F-{len(unique_faces)} E-{len(unique_edges)} V-{len(unique_vertices)}') 644 | 645 | return [unique_faces, unique_edges, FaceEdgeAdj, EdgeVertexAdj] 646 | 647 | 648 | class STModel(nn.Module): 649 | def __init__(self, num_edge, num_surf): 650 | super().__init__() 651 | self.edge_t = nn.Parameter(torch.zeros((num_edge, 3))) 652 | self.surf_st = nn.Parameter(torch.FloatTensor([1,0,0,0]).unsqueeze(0).repeat(num_surf,1)) 653 | 654 | 655 | def get_bbox_minmax(point_cloud): 656 | # Find the minimum and maximum coordinates along each axis 657 | min_x = np.min(point_cloud[:, 0]) 658 | max_x = np.max(point_cloud[:, 0]) 659 | 660 | min_y = np.min(point_cloud[:, 1]) 661 | max_y = np.max(point_cloud[:, 1]) 662 | 663 | min_z = np.min(point_cloud[:, 2]) 664 | max_z = np.max(point_cloud[:, 2]) 665 | 666 | # Create the 3D bounding box using the min and max values 667 | min_point = np.array([min_x, min_y, min_z]) 668 | max_point = np.array([max_x, max_y, max_z]) 669 | return (min_point, max_point) 670 | 671 | 672 | def joint_optimize(surf_ncs, edge_ncs, surfPos, unique_vertices, EdgeVertexAdj, FaceEdgeAdj, num_edge, num_surf): 673 | """ 674 | Jointly optimize the face/edge/vertex based on topology 675 | """ 676 | loss_func = ChamferDistance() 677 | 678 | model = STModel(num_edge, num_surf) 679 | model = model.cuda().train() 680 | optimizer = torch.optim.AdamW( 681 | model.parameters(), 682 | lr=1e-3, 683 | betas=(0.95, 0.999), 684 | weight_decay=1e-6, 685 | eps=1e-08, 686 | ) 687 | 688 | # Optimize edges (directly compute) 689 | edge_ncs_se = edge_ncs[:,[0,-1]] 690 | edge_vertex_se = unique_vertices[EdgeVertexAdj] 691 | 692 | edge_wcs = [] 693 | print('Joint Optimization...') 694 | for wcs, ncs_se, vertex_se in zip(edge_ncs, edge_ncs_se, edge_vertex_se): 695 | # scale 696 | scale_target = np.linalg.norm(vertex_se[0] - vertex_se[1]) 697 | scale_ncs = np.linalg.norm(ncs_se[0] - ncs_se[1]) 698 | edge_scale = scale_target / scale_ncs 699 | 700 | edge_updated = wcs*edge_scale 701 | edge_se = ncs_se*edge_scale 702 | 703 | # offset 704 | offset = (vertex_se - edge_se) 705 | offset_rev = (vertex_se - edge_se[::-1]) 706 | 707 | # swap start / end if necessary 708 | offset_error = np.abs(offset[0] - offset[1]).mean() 709 | offset_rev_error =np.abs(offset_rev[0] - offset_rev[1]).mean() 710 | if offset_rev_error < offset_error: 711 | edge_updated = edge_updated[::-1] 712 | offset = offset_rev 713 | 714 | edge_updated = edge_updated + offset.mean(0)[np.newaxis,np.newaxis,:] 715 | edge_wcs.append(edge_updated) 716 | 717 | edge_wcs = np.vstack(edge_wcs) 718 | 719 | # Replace start/end points with corner, and backprop change along curve 720 | for index in range(len(edge_wcs)): 721 | start_vec = edge_vertex_se[index,0] - edge_wcs[index, 0] 722 | end_vec = edge_vertex_se[index,1] - edge_wcs[index, -1] 723 | weight = np.tile((np.arange(32)/31)[:,np.newaxis], (1,3)) 724 | weighted_vec = np.tile(start_vec[np.newaxis,:],(32,1))*(1-weight) + np.tile(end_vec,(32,1))*weight 725 | edge_wcs[index] += weighted_vec 726 | 727 | # Optimize surfaces 728 | face_edges = [] 729 | for adj in FaceEdgeAdj: 730 | all_pnts = edge_wcs[adj] 731 | face_edges.append(torch.FloatTensor(all_pnts).cuda()) 732 | 733 | # Initialize surface in wcs based on surface pos 734 | surf_wcs_init = [] 735 | bbox_threshold_min = [] 736 | bbox_threshold_max = [] 737 | for edges_perface, ncs, bbox in zip(face_edges, surf_ncs, surfPos): 738 | surf_center, surf_scale = compute_bbox_center_and_size(bbox[0:3], bbox[3:]) 739 | edges_perface_flat = edges_perface.reshape(-1, 3).detach().cpu().numpy() 740 | min_point, max_point = get_bbox_minmax(edges_perface_flat) 741 | edge_center, edge_scale = compute_bbox_center_and_size(min_point, max_point) 742 | bbox_threshold_min.append(min_point) 743 | bbox_threshold_max.append(max_point) 744 | 745 | # increase surface size if does not fully cover the wire bbox 746 | if surf_scale < edge_scale: 747 | surf_scale = 1.05*edge_scale 748 | 749 | wcs = ncs * (surf_scale/2) + surf_center 750 | surf_wcs_init.append(wcs) 751 | 752 | surf_wcs_init = np.stack(surf_wcs_init) 753 | 754 | # optimize the surface offset 755 | surf = torch.FloatTensor(surf_wcs_init).cuda() 756 | for iters in range(200): 757 | surf_scale = model.surf_st[:,0].reshape(-1,1,1,1) 758 | surf_offset = model.surf_st[:,1:].reshape(-1,1,1,3) 759 | surf_updated = surf + surf_offset 760 | 761 | surf_loss = 0 762 | for surf_pnt, edge_pnts in zip(surf_updated, face_edges): 763 | surf_pnt = surf_pnt.reshape(-1,3) 764 | edge_pnts = edge_pnts.reshape(-1,3).detach() 765 | surf_loss += loss_func(surf_pnt.unsqueeze(0), edge_pnts.unsqueeze(0), bidirectional=False, reverse=True) 766 | surf_loss /= len(surf_updated) 767 | 768 | optimizer.zero_grad() 769 | (surf_loss).backward() 770 | optimizer.step() 771 | 772 | # print(f'Iter {iters} surf:{surf_loss:.5f}') 773 | 774 | surf_wcs = surf_updated.detach().cpu().numpy() 775 | 776 | return (surf_wcs, edge_wcs) 777 | 778 | 779 | def add_pcurves_to_edges(face): 780 | edge_fixer = ShapeFix_Edge() 781 | top_exp = TopologyExplorer(face) 782 | for wire in top_exp.wires(): 783 | wire_exp = WireExplorer(wire) 784 | for edge in wire_exp.ordered_edges(): 785 | edge_fixer.FixAddPCurve(edge, face, False, 0.001) 786 | 787 | 788 | def fix_wires(face, debug=False): 789 | top_exp = TopologyExplorer(face) 790 | for wire in top_exp.wires(): 791 | if debug: 792 | wire_checker = ShapeAnalysis_Wire(wire, face, 0.01) 793 | print(f"Check order 3d {wire_checker.CheckOrder()}") 794 | print(f"Check 3d gaps {wire_checker.CheckGaps3d()}") 795 | print(f"Check closed {wire_checker.CheckClosed()}") 796 | print(f"Check connected {wire_checker.CheckConnected()}") 797 | wire_fixer = ShapeFix_Wire(wire, face, 0.01) 798 | 799 | # wire_fixer.SetClosedWireMode(True) 800 | # wire_fixer.SetFixConnectedMode(True) 801 | # wire_fixer.SetFixSeamMode(True) 802 | 803 | assert wire_fixer.IsReady() 804 | ok = wire_fixer.Perform() 805 | # assert ok 806 | 807 | 808 | def fix_face(face): 809 | fixer = ShapeFix_Face(face) 810 | fixer.SetPrecision(0.01) 811 | fixer.SetMaxTolerance(0.1) 812 | ok = fixer.Perform() 813 | # assert ok 814 | fixer.FixOrientation() 815 | face = fixer.Face() 816 | return face 817 | 818 | 819 | def construct_brep(surf_wcs, edge_wcs, FaceEdgeAdj, EdgeVertexAdj): 820 | """ 821 | Fit parametric surfaces / curves and trim into B-rep 822 | """ 823 | print('Building the B-rep...') 824 | # Fit surface bspline 825 | recon_faces = [] 826 | for points in surf_wcs: 827 | num_u_points, num_v_points = 32, 32 828 | uv_points_array = TColgp_Array2OfPnt(1, num_u_points, 1, num_v_points) 829 | for u_index in range(1,num_u_points+1): 830 | for v_index in range(1,num_v_points+1): 831 | pt = points[u_index-1, v_index-1] 832 | point_3d = gp_Pnt(float(pt[0]), float(pt[1]), float(pt[2])) 833 | uv_points_array.SetValue(u_index, v_index, point_3d) 834 | approx_face = GeomAPI_PointsToBSplineSurface(uv_points_array, 3, 8, GeomAbs_C2, 5e-2).Surface() 835 | recon_faces.append(approx_face) 836 | 837 | recon_edges = [] 838 | for points in edge_wcs: 839 | num_u_points = 32 840 | u_points_array = TColgp_Array1OfPnt(1, num_u_points) 841 | for u_index in range(1,num_u_points+1): 842 | pt = points[u_index-1] 843 | point_2d = gp_Pnt(float(pt[0]), float(pt[1]), float(pt[2])) 844 | u_points_array.SetValue(u_index, point_2d) 845 | try: 846 | approx_edge = GeomAPI_PointsToBSpline(u_points_array, 0, 8, GeomAbs_C2, 5e-3).Curve() 847 | except Exception as e: 848 | print('high precision failed, trying mid precision...') 849 | try: 850 | approx_edge = GeomAPI_PointsToBSpline(u_points_array, 0, 8, GeomAbs_C2, 8e-3).Curve() 851 | except Exception as e: 852 | print('mid precision failed, trying low precision...') 853 | approx_edge = GeomAPI_PointsToBSpline(u_points_array, 0, 8, GeomAbs_C2, 5e-2).Curve() 854 | recon_edges.append(approx_edge) 855 | 856 | # Create edges from the curve list 857 | edge_list = [] 858 | for curve in recon_edges: 859 | edge = BRepBuilderAPI_MakeEdge(curve).Edge() 860 | edge_list.append(edge) 861 | 862 | # Cut surface by wire 863 | post_faces = [] 864 | post_edges = [] 865 | for idx,(surface, edge_incides) in enumerate(zip(recon_faces, FaceEdgeAdj)): 866 | corner_indices = EdgeVertexAdj[edge_incides] 867 | 868 | # ordered loop 869 | loops = [] 870 | ordered = [0] 871 | seen_corners = [corner_indices[0,0], corner_indices[0,1]] 872 | next_index = corner_indices[0,1] 873 | 874 | while len(ordered)