├── framework.jpg ├── feature_extract_pc ├── demo.py ├── feature_extract.py └── nss_functions.py ├── LICENSE ├── README.md ├── experiment for SJTU-PCQA ├── performance.py └── mos.csv └── experiment for WPC ├── performance.py └── mos.csv /framework.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zzc-1998/NR-3DQA/HEAD/framework.jpg -------------------------------------------------------------------------------- /feature_extract_pc/demo.py: -------------------------------------------------------------------------------- 1 | from feature_extract import get_feature_vector 2 | import time 3 | 4 | #demo 5 | objpath = "models/hhi_5.ply" 6 | start = time.time() 7 | features = get_feature_vector(objpath) 8 | end = time.time() 9 | time_cost = end-start 10 | 11 | 12 | #show the features 13 | cnt = 0 14 | for feature_domain in ['l','a','b','curvature','anisotropy','linearity','planarity','sphericity']: 15 | for param in ["mean","std","entropy","ggd1","ggd2","aggd1","aggd2","aggd3","aggd4","gamma1","gamma2"]: 16 | print(feature_domain + "_" + param + ": " + str(features[cnt])) 17 | cnt = cnt + 1 18 | print("Cost " + str(time_cost) + " sec.") 19 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 zzc-1998 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Paper 2 | This is the code for "No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models" and it is the point cloud version. This paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technology. 3 | The arxiv version can be found here [http://arxiv.org/abs/2107.02041] and the TCVST version can be found here [https://ieeexplore.ieee.org/document/9810024]. 4 | 5 | 6 | 7 | # How to start with the code? 8 | You should get the h5py, pyntcloud, skimage package by 9 | 10 | ``` 11 | pip install h5py 12 | pip install pyntcloud 13 | pip install scikit-image 14 | ``` 15 | 16 | ## Environment settings 17 | We test the code with Python 3.7 (and higher) on the Windows platform and the code may run on linux as well. 18 | 19 | ## Demo 20 | The **demo.py** includes a demo for the feature extraction of colored point cloud and we provide with a colored point cloud sample **hhi_5.ply**. 21 | 22 | ## Database 23 | The code is tested on the SJTU-PCQA database which can be downloaded at [https://smt.sjtu.edu.cn/]. 24 | 25 | The code is also tested on the WPC database which can be downloaded at [https://github.com/qdushl/Waterloo-Point-Cloud-Database]. 26 | 27 | ## Experiment Update 28 | 29 | We update the experiment files for SJTU-PCQA and WPC databases, which includes the MOSs and extracted features from point clouds. 30 | We do not use the GGD, AGGD, Gamma parameters of color features in this experiment version for simplification. 31 | 32 | ## Other PCQA works 33 | We implement and collect several common PCQA metrics, which can be accessed [here](https://github.com/zzc-1998/Point-cloud-quality-assessment). 34 | 35 | # Citation 36 | If you find our work useful, please cite our work as: 37 | ``` 38 | @ARTICLE{zhang2022no, 39 | author={Zhang, Zicheng and Sun, Wei and Min, Xiongkuo and Wang, Tao and Lu, Wei and Zhai, Guangtao}, 40 | journal={IEEE Transactions on Circuits and Systems for Video Technology}, 41 | title={No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models}, 42 | year={2022}, 43 | volume={}, 44 | number={}, 45 | pages={1-1}, 46 | doi={10.1109/TCSVT.2022.3186894}} 47 | ``` 48 | If you have further questions, please email us through **zzc1998@sjtu.edu.cn**. 49 | -------------------------------------------------------------------------------- /feature_extract_pc/feature_extract.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | from skimage import color 4 | from nss_functions import * 5 | from pyntcloud import PyntCloud 6 | import os 7 | 8 | def get_feature_vector(objpath): 9 | #load colored point cloud 10 | print("Begin loading point cloud") 11 | cloud = PyntCloud.from_file(objpath) 12 | 13 | #begin geometry projection 14 | print("Begin geometry feature extraction.") 15 | k_neighbors = cloud.get_neighbors(k=10) 16 | ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors) 17 | cloud.add_scalar_field("curvature", ev=ev) 18 | cloud.add_scalar_field("anisotropy",ev=ev) 19 | cloud.add_scalar_field("linearity",ev=ev) 20 | cloud.add_scalar_field("planarity",ev=ev) 21 | cloud.add_scalar_field("sphericity",ev=ev) 22 | curvature = cloud.points['curvature(11)'].to_numpy() 23 | anisotropy = cloud.points['anisotropy(11)'].to_numpy() 24 | linearity = cloud.points['linearity(11)'].to_numpy() 25 | planarity = cloud.points['planarity(11)'].to_numpy() 26 | sphericity = cloud.points['sphericity(11)'].to_numpy() 27 | 28 | 29 | #begin color projection 30 | print("Begin color feature extraction.") 31 | rgb_color = cloud.points[['red','green','blue']].to_numpy()/255 32 | lab_color = color.rgb2lab(rgb_color) 33 | l = lab_color[:,0] 34 | a = lab_color[:,1] 35 | b = lab_color[:,2] 36 | 37 | 38 | print("Begin NSS parameters estimation.") 39 | # compute nss parameters 40 | nss_params = [] 41 | # compute color nss features 42 | for tmp in [l,a,b]: 43 | params = get_color_nss_param(tmp) 44 | #flatten the feature vector 45 | nss_params = nss_params + [i for item in params for i in item] 46 | # compute geomerty nss features 47 | for tmp in [curvature,anisotropy,linearity,planarity,sphericity]: 48 | params = get_geometry_nss_param(tmp) 49 | #flatten the feature vector 50 | nss_params = nss_params + [i for item in params for i in item] 51 | return nss_params 52 | 53 | #demo 54 | objpath = "models/hhi_5.ply" 55 | features = get_feature_vector(objpath) 56 | 57 | #show the features 58 | cnt = 0 59 | for feature_domain in ['l','a','b']: 60 | for param in ["mean","std","entropy"]: 61 | print(feature_domain + "_" + param + ": " + str(features[cnt])) 62 | cnt = cnt + 1 63 | for feature_domain in ['curvature','anisotropy','linearity','planarity','sphericity']: 64 | for param in ["mean","std","entropy","ggd1","ggd2","aggd1","aggd2","aggd3","aggd4","gamma1","gamma2"]: 65 | print(feature_domain + "_" + param + ": " + str(features[cnt])) 66 | cnt = cnt + 1 67 | -------------------------------------------------------------------------------- /experiment for SJTU-PCQA/performance.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.svm import SVR 3 | from sklearn.preprocessing import MinMaxScaler 4 | import pandas as pd 5 | from scipy import stats 6 | 7 | # get data according to the train test name lists, return scaled train and test set 8 | def get_data(train_name_list,test_name_list): 9 | feature_data = pd.read_csv("features.csv",index_col = 0,keep_default_na=False) 10 | feature_data = feature_data[feature_data.columns.values] 11 | score_data = pd.read_csv("mos.csv") 12 | train_set = [] 13 | train_score = [] 14 | test_set = [] 15 | test_score = [] 16 | for name in train_name_list: 17 | score = score_data[name].tolist() 18 | train_score = train_score + score 19 | for i in range(42): 20 | name_pc = name+str(i) 21 | data = feature_data.loc[name_pc,:].tolist() 22 | train_set.append(data) 23 | 24 | for name in test_name_list: 25 | score = score_data[name].tolist() 26 | test_score = test_score + score 27 | for i in range(42): 28 | name_pc = name+str(i) 29 | data = feature_data.loc[name_pc,:].tolist() 30 | test_set.append(data) 31 | # preprocessing 32 | scaler = MinMaxScaler() 33 | train_set = scaler.fit_transform(train_set) 34 | test_set = scaler.transform(test_set) 35 | return train_set,np.array(train_score)/10,test_set,np.array(test_score)/10 36 | 37 | 38 | 39 | 40 | if __name__ == '__main__': 41 | plcc = [] 42 | srcc =[] 43 | krcc = [] 44 | cnt = 0 45 | # begin 9-folder cross data validation split 46 | for i in range(9): 47 | cnt =cnt+1 48 | print(cnt) 49 | # generate train_name_list and test_name_list 50 | train_name_list = ['redandblack','Romanoillamp','loot','soldier','ULB Unicorn','longdress','statue','shiva','hhi'] 51 | test_name_list = [train_name_list.pop(i)] 52 | # get data 53 | print('Begin split ' + str(i+1) + ' and use the following list as test set:') 54 | print(test_name_list) 55 | train_set,train_score,test_set,test_score = get_data(train_name_list,test_name_list) 56 | # begin training 57 | print('Begin training!') 58 | svr = SVR(kernel='rbf') 59 | svr.fit(train_set, train_score) 60 | predict_score = svr.predict(test_set) 61 | # record the result 62 | plcc.append(stats.pearsonr(predict_score, test_score)[0]) 63 | srcc.append(stats.spearmanr(predict_score, test_score)[0]) 64 | krcc.append(stats.stats.kendalltau(predict_score, test_score)[0]) 65 | print('Training complete!') 66 | print('------------------------------------------------------------------------------------------------------------------') 67 | print('------------------------------------------------------------------------------------------------------------------') 68 | print('Final Results presentation:') 69 | 70 | print("PLCC: "+ str(sum(plcc)/len(plcc))) 71 | print("SRCC: "+ str(sum(srcc)/len(srcc))) 72 | print("KRCC: "+ str(sum(krcc)/len(krcc))) 73 | 74 | -------------------------------------------------------------------------------- /experiment for WPC/performance.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sklearn.svm import SVR 3 | from sklearn.preprocessing import MinMaxScaler 4 | import pandas as pd 5 | from scipy import stats 6 | 7 | name_list = ['bag','banana','biscuits','cake','cauliflower','flowerpot','glasses_case','honeydew_melon','house','litchi','mushroom','pen_container','pineapple','ping-pong_bat','puer_tea','pumpkin','ship','statue','stone','tool_box'] 8 | 9 | 10 | # get data according to the train test name lists, return scaled train and test set 11 | def get_data(train_name_list,test_name_list): 12 | feature_data = pd.read_csv("features.csv",index_col = 0,keep_default_na=False) 13 | feature_data = feature_data[feature_data.columns.values] 14 | score_data = pd.read_csv("mos.csv") 15 | mos = score_data['mos'].tolist() 16 | total_obj_names = score_data['name'] 17 | score_data = pd.read_csv("mos.csv",index_col = 0) 18 | train_set = [] 19 | train_score = [] 20 | test_set = [] 21 | test_score = [] 22 | for name in train_name_list: 23 | obj_names = [] 24 | for obj in total_obj_names: 25 | if name in obj: 26 | obj_names.append(obj) 27 | for i in obj_names: 28 | data = feature_data.loc[i,:].tolist() 29 | train_set.append(data) 30 | train_score.append(score_data.loc[i,:].tolist()[0]) 31 | 32 | for name in test_name_list: 33 | obj_names = [] 34 | for obj in total_obj_names: 35 | if name in obj: 36 | obj_names.append(obj) 37 | for i in obj_names: 38 | data = feature_data.loc[i,:].tolist() 39 | test_set.append(data) 40 | test_score.append(score_data.loc[i,:].tolist()[0]) 41 | scaler = MinMaxScaler() 42 | train_set = scaler.fit_transform(train_set) 43 | test_set = scaler.transform(test_set) 44 | return train_set,np.array(train_score)/100,test_set,np.array(test_score)/100 45 | 46 | 47 | 48 | 49 | 50 | if __name__ == '__main__': 51 | plcc = [] 52 | srcc =[] 53 | krcc = [] 54 | for i in range(5): 55 | # generate 5 folder cross validation split name lists 56 | train_name_list = name_list.copy() 57 | # get test set and remove the test content from the training set 58 | test_name_list = [train_name_list.pop(4*i + 3),train_name_list.pop(4*i + 2),train_name_list.pop(4*i + 1),train_name_list.pop(4*i)] 59 | print('Begin split ' + str(i+1) + ' and use the following list as test set:') 60 | print(test_name_list) 61 | # get the data according to the name lists 62 | train_set,train_score,test_set,test_score = get_data(train_name_list,test_name_list) 63 | # begin training and predicting 64 | print('Begin training!') 65 | svr = SVR(kernel='rbf') 66 | svr.fit(train_set, train_score) 67 | predict_score = svr.predict(test_set) 68 | # record the result 69 | plcc.append(stats.pearsonr(predict_score, test_score)[0]) 70 | srcc.append(stats.spearmanr(predict_score, test_score)[0]) 71 | krcc.append(stats.stats.kendalltau(predict_score, test_score)[0]) 72 | print('Training complete!') 73 | print('------------------------------------------------------------------------------------------------------------------') 74 | print('------------------------------------------------------------------------------------------------------------------') 75 | print('Final Results presentation:') 76 | print("SRCC: "+ str(sum(srcc)/len(srcc))) 77 | print("PLCC: "+ str(sum(plcc)/len(plcc))) 78 | print("KRCC: "+ str(sum(krcc)/len(krcc))) 79 | 80 | 81 | -------------------------------------------------------------------------------- /feature_extract_pc/nss_functions.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy 3 | from scipy import stats 4 | import pandas as pd 5 | from scipy.stats import entropy 6 | from scipy.special import gamma 7 | from sklearn.preprocessing import scale,normalize 8 | def my_scale(vec): 9 | vec = (vec-np.mean(vec))/np.std(vec, ddof=1) 10 | return vec 11 | 12 | def get_color_nss_param(vec): 13 | """Estimate color NSS parameters. 14 | :param vec: The vector that we want to approximate its parameter. 15 | :type vec: np.ndarray 16 | :scale is the normalization function 17 | """ 18 | return [estimate_basic_param(vec)] 19 | 20 | 21 | def get_geometry_nss_param(vec): 22 | """Estimate geometry NSS parameters. 23 | :param vec: The vector that we want to approximate its parameter. 24 | :type vec: np.ndarray 25 | :scale is the normalization function 26 | """ 27 | return [estimate_basic_param(vec),estimate_ggd_param(vec),estimate_aggd_param(my_scale(vec)),estimate_gamma_param(vec)] 28 | 29 | def Entropy(labels): 30 | #probs = pd.Series(labels).value_counts() / len(labels) 31 | probs = pd.Series(labels).value_counts(bins = 2000) / len(labels) 32 | en = stats.entropy(probs) 33 | return en 34 | 35 | 36 | def estimate_basic_param(vec): 37 | """Estimate basic parameter. 38 | :param vec: The vector that we want to approximate its parameter. 39 | :type vec: np.ndarray 40 | """ 41 | result = [np.mean(vec),np.std(vec, ddof=1),Entropy(vec)] 42 | return result 43 | 44 | def estimate_ggd_param(vec): 45 | """Estimate GGD parameter. 46 | :param vec: The vector that we want to approximate its parameter. 47 | :type vec: np.ndarray 48 | """ 49 | gam = np.arange(0.2, 10 + 0.001, 0.001) 50 | r_gam = (gamma(1.0 / gam) * gamma(3.0 / gam) / (gamma(2.0 / gam) ** 2)) 51 | 52 | sigma_sq = np.mean(vec ** 2) 53 | sigma = np.sqrt(sigma_sq) 54 | E = np.mean(np.abs(vec)) 55 | rho = sigma_sq / E ** 2 56 | 57 | differences = abs(rho - r_gam) 58 | array_position = np.argmin(differences) 59 | gamparam = gam[array_position] 60 | result = [gamparam, sigma] 61 | return result 62 | 63 | def estimate_aggd_param(vec): 64 | """Estimate AGGD parameter. 65 | :param vec: The vector that we want to approximate its parameter. 66 | :type vec: np.ndarray 67 | """ 68 | gam = np.arange(0.2, 10 + 0.001, 0.001) 69 | r_gam = ((gamma(2.0 / gam)) ** 2) / ( 70 | gamma(1.0 / gam) * gamma(3.0 / gam)) 71 | 72 | left_std = np.sqrt(np.mean((vec[vec < 0]) ** 2)) 73 | right_std = np.sqrt(np.mean((vec[vec > 0]) ** 2)) 74 | gamma_hat = left_std / right_std 75 | rhat = (np.mean(np.abs(vec))) ** 2 / np.mean((vec) ** 2) 76 | rhat_norm = (rhat * (gamma_hat ** 3 + 1) * (gamma_hat + 1)) / ( 77 | (gamma_hat ** 2 + 1) ** 2) 78 | 79 | differences = (r_gam - rhat_norm) ** 2 80 | array_position = np.argmin(differences) 81 | alpha = gam[array_position] 82 | const = np.sqrt(gamma(1 / alpha)) / np.sqrt(gamma(3 / alpha)) 83 | mean_param = (right_std - left_std) * ( 84 | gamma(2 / alpha) / gamma(1 / alpha)) * const 85 | result = [alpha, mean_param,left_std, right_std] 86 | return result 87 | 88 | 89 | def estimate_gamma_param(vec): 90 | """Estimate Gamma parameter. 91 | :param vec: The vector that we want to approximate its parameter. 92 | :type vec: np.ndarray 93 | """ 94 | mean = np.mean(vec) 95 | std = np.std(vec) 96 | shape = (mean/std)**2 97 | scale = (std**2)/mean 98 | result = [shape,scale] 99 | return result 100 | -------------------------------------------------------------------------------- /experiment for SJTU-PCQA/mos.csv: -------------------------------------------------------------------------------- 1 | redandblack,Romanoillamp,loot,soldier,ULB Unicorn,longdress,statue,shiva,hhi 2 | 8.167091837,8.220663265,6.996153846,8.3203125,9.116666667,8.50127551,6.908333333,6.558333333,9.516666667 3 | 6.45,7.405769231,7.0859375,5.783333333,9.321428571,6.159615385,7.6328125,7.834615385,7.7109375 4 | 6.734375,6.975,5.383333333,5.832692308,9.682692308,6.3046875,6.168367347,7.767307692,8.9453125 5 | 4.9,6.1015625,5.208333333,6.175,8.62244898,5.5078125,4.93877551,5.991666667,7.371153846 6 | 6.286989796,4.963461538,6.679846939,5.083333333,7.691326531,7.117346939,3.015625,6.6171875,4.183333333 7 | 3.7578125,2.380102041,2.708333333,3.835459184,3.6171875,3.40625,1.453125,3.262755102,1.953125 8 | 9.392857143,7.688461538,9.65,9.583333333,8.771683673,9.682692308,7.40625,8.5,9.796875 9 | 7.608333333,8.3671875,7.316666667,6.950255102,7.568877551,7.526923077,5.459183673,6.917091837,6.776923077 10 | 7.24744898,6.553571429,5.396683673,5.813461538,6.619897959,6.709615385,6.4,7.844230769,6.107692308 11 | 6.53125,6.880769231,4.584615385,6.069230769,7.808333333,4.927295918,5.899234694,7.62244898,6.140625 12 | 5.515625,6.348076923,4.230867347,4.516666667,6.953125,4.90625,6.588461538,6.784615385,4.808333333 13 | 4.780769231,5.9453125,4.0625,3.909438776,6.2265625,4.091666667,5.107692308,6.244897959,5.021153846 14 | 7.767307692,7.352040816,8.267307692,7.615384615,7.941666667,8.161538462,7.825,6.975,8.741666667 15 | 6.803846154,5.857692308,8.613520408,8.5234375,8.163461538,8.381377551,6.133333333,6.816666667,6.534615385 16 | 6.65,6.226923077,7.676020408,7.68877551,8.25,7.744897959,6.054846939,6.591666667,7.015625 17 | 5.1640625,5.525510204,5.78125,5.623076923,7.544230769,5.7578125,3.965384615,6.331632653,4.9296875 18 | 3.709615385,3.5859375,2.975,4.1171875,5.125,4.421875,3.4921875,5.943877551,4.056122449 19 | 2.141666667,1.848076923,1.85,1.934615385,2.723076923,2.126923077,1.1875,3.4609375,1.785714286 20 | 8.191666667,6.841666667,8.180769231,9.178571429,9.553571429,9.171875,7.141666667,7.401785714,8.238461538 21 | 6.85,6.5703125,6.925,8.1328125,8.71875,6.783333333,6.762755102,6.833333333,7.399234694 22 | 6.144132653,5.141666667,4.8,5.765625,7.296153846,5.849489796,6.1328125,7.9921875,5.016666667 23 | 4.6484375,4.234375,4.299744898,4.463461538,6.198076923,4.136538462,3.441326531,6.001923077,4.308333333 24 | 3.465384615,3.455769231,2.283333333,3.1640625,6.667091837,3.62372449,2.5546875,5.0078125,2.4453125 25 | 1.433333333,1.37755102,1.223076923,1.491666667,2.6,1.790384615,1,2.955357143,1.905769231 26 | 8.2578125,7.2,7.190384615,7.816666667,8.008333333,7.833333333,5.81377551,6.525,7.680769231 27 | 7.3515625,5.9296875,7.28125,5.833333333,8.7734375,6.640384615,5.808673469,7.78125,6.366666667 28 | 5.579081633,4.6328125,4.166666667,4.5546875,6.533333333,5.166666667,4.765625,5.724489796,4.5625 29 | 2.4375,2.067602041,2.376923077,1.7265625,3.125,3.191666667,1.916666667,3.3984375,2.208333333 30 | 2.275,1.5,1.703846154,1.781887755,2.411989796,1.4453125,2.073979592,2.021153846,1.540384615 31 | 1,1.223076923,1,1,1.433333333,1.075,1,1.216666667,1 32 | 8.125,7.767857143,7.341666667,7.825,9.40625,7.358333333,6.795918367,8.8359375,9.464285714 33 | 6.441666667,7.155612245,7.765306122,7.6640625,7.008333333,6.985969388,6.525,6.825,8.3125 34 | 5.9609375,4.590561224,4.775,4.334615385,6.166666667,4.55,6.536989796,5.75,6.836734694 35 | 3.394132653,2.609375,2.825,2.280769231,3.93622449,2.515625,2.85,2.830769231,3.475 36 | 1.866666667,1.857692308,2.492346939,1.8671875,3.7734375,1.987244898,1.625,2.320153061,1.867307692 37 | 1.848076923,1.3,1.223076923,1.234693878,1.5703125,1.448979592,1.69005102,1.600765306,1.1953125 38 | 8.525,8.375,8.344230769,8.65,8.433333333,8.325,8.121153846,7.084183673,8.219230769 39 | 6.063461538,7.066326531,6.7,7.084183673,8.5625,6.008333333,6.784615385,6.673469388,5.842307692 40 | 5.96875,5.15,4.25,4.358333333,7.1953125,4.825,5.184615385,5.828125,4.920918367 41 | 3.125,3.103316327,2.758333333,2.75,3.545918367,2.330769231,3.1796875,3.733333333,2.4921875 42 | 1.876923077,2.2,1.934615385,2.320153061,2.811538462,1.783333333,2.463010204,3.041666667,2.083333333 43 | 1.691666667,1.306122449,1.2,1.4140625,1.223076923,1.433333333,1.617307692,2.108333333,1.5 44 | -------------------------------------------------------------------------------- /experiment for WPC/mos.csv: -------------------------------------------------------------------------------- 1 | name,mos 2 | bag_gQP_1_tQP_1.ply,86.69456186 3 | bag_gQP_1_tQP_2.ply,76.14530845 4 | bag_gQP_1_tQP_3.ply,52.85558429 5 | bag_gQP_2_tQP_1.ply,52.1814527 6 | bag_gQP_2_tQP_2.ply,49.31511353 7 | bag_gQP_2_tQP_3.ply,39.10736912 8 | bag_gQP_3_tQP_1.ply,27.44033309 9 | bag_gQP_3_tQP_2.ply,32.01772343 10 | bag_gQP_3_tQP_3.ply,29.61486469 11 | bag_gsigma_0_tsigma_16.ply,86.24911138 12 | bag_gsigma_0_tsigma_32.ply,62.11964039 13 | bag_gsigma_0_tsigma_8.ply,88.78171897 14 | bag_gsigma_2_tsigma_16.ply,49.36475509 15 | bag_gsigma_2_tsigma_32.ply,43.75380957 16 | bag_gsigma_2_tsigma_8.ply,56.92145553 17 | bag_gsigma_4_tsigma_16.ply,21.98784769 18 | bag_gsigma_4_tsigma_32.ply,22.43616482 19 | bag_gsigma_4_tsigma_8.ply,22.37620413 20 | bag_level_7.ply,9.59532456 21 | bag_level_8.ply,18.23796415 22 | bag_level_9.ply,82.74107891 23 | bag_pqs_1_qs_16.ply,90.340738 24 | bag_pqs_1_qs_32.ply,81.83884571 25 | bag_pqs_1_qs_48.ply,58.43553789 26 | bag_pqs_1_qs_64.ply,62.55491922 27 | bag_tsl_4_tqs_128.ply,62.21628821 28 | bag_tsl_4_tqs_256.ply,36.26609788 29 | bag_tsl_4_tqs_512.ply,20.84981283 30 | bag_tsl_4_tqs_64.ply,67.09168867 31 | bag_tsl_6_tqs_128.ply,48.88989054 32 | bag_tsl_6_tqs_256.ply,40.2873935 33 | bag_tsl_6_tqs_512.ply,24.60106839 34 | bag_tsl_6_tqs_64.ply,58.51287376 35 | bag_tsl_8_tqs_128.ply,80.66164159 36 | bag_tsl_8_tqs_256.ply,60.38564447 37 | bag_tsl_8_tqs_512.ply,34.95747094 38 | bag_tsl_8_tqs_64.ply,90.36817669 39 | banana_gQP_1_tQP_1.ply,60.30135594 40 | banana_gQP_1_tQP_2.ply,56.05951863 41 | banana_gQP_1_tQP_3.ply,52.14230175 42 | banana_gQP_2_tQP_1.ply,43.47696839 43 | banana_gQP_2_tQP_2.ply,43.98226716 44 | banana_gQP_2_tQP_3.ply,39.80857854 45 | banana_gQP_3_tQP_1.ply,29.10726664 46 | banana_gQP_3_tQP_2.ply,23.95632388 47 | banana_gQP_3_tQP_3.ply,22.65939287 48 | banana_gsigma_0_tsigma_16.ply,60.4722494 49 | banana_gsigma_0_tsigma_32.ply,40.11307634 50 | banana_gsigma_0_tsigma_8.ply,83.0931383 51 | banana_gsigma_2_tsigma_16.ply,49.66770221 52 | banana_gsigma_2_tsigma_32.ply,31.34629454 53 | banana_gsigma_2_tsigma_8.ply,50.75512514 54 | banana_gsigma_4_tsigma_16.ply,27.01710822 55 | banana_gsigma_4_tsigma_32.ply,23.38349674 56 | banana_gsigma_4_tsigma_8.ply,31.97703197 57 | banana_level_7.ply,7.109028314 58 | banana_level_8.ply,23.08348257 59 | banana_level_9.ply,89.49153987 60 | banana_pqs_1_qs_16.ply,82.92557282 61 | banana_pqs_1_qs_32.ply,78.10954901 62 | banana_pqs_1_qs_48.ply,61.89404966 63 | banana_pqs_1_qs_64.ply,40.01541846 64 | banana_tsl_4_tqs_128.ply,52.49538462 65 | banana_tsl_4_tqs_256.ply,34.18431286 66 | banana_tsl_4_tqs_512.ply,29.94302564 67 | banana_tsl_4_tqs_64.ply,54.12092417 68 | banana_tsl_6_tqs_128.ply,79.9250077 69 | banana_tsl_6_tqs_256.ply,61.62952635 70 | banana_tsl_6_tqs_512.ply,35.85265105 71 | banana_tsl_6_tqs_64.ply,83.18362987 72 | banana_tsl_8_tqs_128.ply,86.14465436 73 | banana_tsl_8_tqs_256.ply,61.90144929 74 | banana_tsl_8_tqs_512.ply,38.96808477 75 | banana_tsl_8_tqs_64.ply,84.62807112 76 | biscuits_gQP_1_tQP_1.ply,80.64772987 77 | biscuits_gQP_1_tQP_2.ply,69.66364063 78 | biscuits_gQP_1_tQP_3.ply,55.29057762 79 | biscuits_gQP_2_tQP_1.ply,68.13405141 80 | biscuits_gQP_2_tQP_2.ply,65.29075052 81 | biscuits_gQP_2_tQP_3.ply,53.1133493 82 | biscuits_gQP_3_tQP_1.ply,67.14806687 83 | biscuits_gQP_3_tQP_2.ply,54.19129873 84 | biscuits_gQP_3_tQP_3.ply,45.67685542 85 | biscuits_gsigma_0_tsigma_16.ply,70.89991405 86 | biscuits_gsigma_0_tsigma_32.ply,40.47262999 87 | biscuits_gsigma_0_tsigma_8.ply,82.28266671 88 | biscuits_gsigma_2_tsigma_16.ply,55.54540274 89 | biscuits_gsigma_2_tsigma_32.ply,37.85852516 90 | biscuits_gsigma_2_tsigma_8.ply,60.93076647 91 | biscuits_gsigma_4_tsigma_16.ply,43.1335575 92 | biscuits_gsigma_4_tsigma_32.ply,25.37935374 93 | biscuits_gsigma_4_tsigma_8.ply,46.58815205 94 | biscuits_level_7.ply,6.910472714 95 | biscuits_level_8.ply,26.07829319 96 | biscuits_level_9.ply,78.90662046 97 | biscuits_pqs_1_qs_16.ply,85.08186966 98 | biscuits_pqs_1_qs_32.ply,74.44788099 99 | biscuits_pqs_1_qs_48.ply,40.20381716 100 | biscuits_pqs_1_qs_64.ply,24.03115802 101 | biscuits_tsl_4_tqs_128.ply,20.56426096 102 | biscuits_tsl_4_tqs_256.ply,17.38710508 103 | biscuits_tsl_4_tqs_512.ply,11.29893963 104 | biscuits_tsl_4_tqs_64.ply,18.04515443 105 | biscuits_tsl_6_tqs_128.ply,68.95568291 106 | biscuits_tsl_6_tqs_256.ply,46.84884777 107 | biscuits_tsl_6_tqs_512.ply,27.49479604 108 | biscuits_tsl_6_tqs_64.ply,83.00914582 109 | biscuits_tsl_8_tqs_128.ply,71.1781634 110 | biscuits_tsl_8_tqs_256.ply,50.5576824 111 | biscuits_tsl_8_tqs_512.ply,24.26462774 112 | biscuits_tsl_8_tqs_64.ply,81.60694704 113 | cake_gQP_1_tQP_1.ply,80.20526502 114 | cake_gQP_1_tQP_2.ply,71.92097573 115 | cake_gQP_1_tQP_3.ply,53.38313209 116 | cake_gQP_2_tQP_1.ply,55.79723355 117 | cake_gQP_2_tQP_2.ply,51.11284661 118 | cake_gQP_2_tQP_3.ply,49.40907712 119 | cake_gQP_3_tQP_1.ply,27.03674282 120 | cake_gQP_3_tQP_2.ply,25.93804334 121 | cake_gQP_3_tQP_3.ply,25.63802684 122 | cake_gsigma_0_tsigma_16.ply,68.48543238 123 | cake_gsigma_0_tsigma_32.ply,44.28105151 124 | cake_gsigma_0_tsigma_8.ply,82.3590312 125 | cake_gsigma_2_tsigma_16.ply,58.68613346 126 | cake_gsigma_2_tsigma_32.ply,40.58216881 127 | cake_gsigma_2_tsigma_8.ply,67.14183949 128 | cake_gsigma_4_tsigma_16.ply,45.00850006 129 | cake_gsigma_4_tsigma_32.ply,33.58645616 130 | cake_gsigma_4_tsigma_8.ply,50.02089902 131 | cake_level_7.ply,7.770366309 132 | cake_level_8.ply,17.08593154 133 | cake_level_9.ply,64.34512167 134 | cake_pqs_1_qs_16.ply,90.78201527 135 | cake_pqs_1_qs_32.ply,62.31185039 136 | cake_pqs_1_qs_48.ply,34.56216851 137 | cake_pqs_1_qs_64.ply,24.85902515 138 | cake_tsl_4_tqs_128.ply,71.41920062 139 | cake_tsl_4_tqs_256.ply,53.06015189 140 | cake_tsl_4_tqs_512.ply,21.54060176 141 | cake_tsl_4_tqs_64.ply,83.76018893 142 | cake_tsl_6_tqs_128.ply,79.74237911 143 | cake_tsl_6_tqs_256.ply,51.50226832 144 | cake_tsl_6_tqs_512.ply,27.39548212 145 | cake_tsl_6_tqs_64.ply,82.75849407 146 | cake_tsl_8_tqs_128.ply,83.19207028 147 | cake_tsl_8_tqs_256.ply,62.14575182 148 | cake_tsl_8_tqs_512.ply,32.96552511 149 | cake_tsl_8_tqs_64.ply,90.65408252 150 | cauliflower_gQP_1_tQP_1.ply,87.62483924 151 | cauliflower_gQP_1_tQP_2.ply,69.93401821 152 | cauliflower_gQP_1_tQP_3.ply,55.86648008 153 | cauliflower_gQP_2_tQP_1.ply,77.6400648 154 | cauliflower_gQP_2_tQP_2.ply,61.72646927 155 | cauliflower_gQP_2_tQP_3.ply,50.22284116 156 | cauliflower_gQP_3_tQP_1.ply,34.47364814 157 | cauliflower_gQP_3_tQP_2.ply,27.41161955 158 | cauliflower_gQP_3_tQP_3.ply,30.23139757 159 | cauliflower_gsigma_0_tsigma_16.ply,57.42510381 160 | cauliflower_gsigma_0_tsigma_32.ply,40.19071723 161 | cauliflower_gsigma_0_tsigma_8.ply,77.76226833 162 | cauliflower_gsigma_2_tsigma_16.ply,53.13171917 163 | cauliflower_gsigma_2_tsigma_32.ply,31.36818616 164 | cauliflower_gsigma_2_tsigma_8.ply,64.1622967 165 | cauliflower_gsigma_4_tsigma_16.ply,44.40470161 166 | cauliflower_gsigma_4_tsigma_32.ply,27.32584159 167 | cauliflower_gsigma_4_tsigma_8.ply,47.85717299 168 | cauliflower_level_7.ply,8.793459623 169 | cauliflower_level_8.ply,16.03060304 170 | cauliflower_level_9.ply,74.68114942 171 | cauliflower_pqs_1_qs_16.ply,90.03846164 172 | cauliflower_pqs_1_qs_32.ply,63.72945614 173 | cauliflower_pqs_1_qs_48.ply,27.07846018 174 | cauliflower_pqs_1_qs_64.ply,14.40334861 175 | cauliflower_tsl_4_tqs_128.ply,42.18869294 176 | cauliflower_tsl_4_tqs_256.ply,33.13006869 177 | cauliflower_tsl_4_tqs_512.ply,26.6104176 178 | cauliflower_tsl_4_tqs_64.ply,61.90399729 179 | cauliflower_tsl_6_tqs_128.ply,64.27065355 180 | cauliflower_tsl_6_tqs_256.ply,41.86633972 181 | cauliflower_tsl_6_tqs_512.ply,29.74216566 182 | cauliflower_tsl_6_tqs_64.ply,75.53395733 183 | cauliflower_tsl_8_tqs_128.ply,79.76526676 184 | cauliflower_tsl_8_tqs_256.ply,43.98790896 185 | cauliflower_tsl_8_tqs_512.ply,27.74439609 186 | cauliflower_tsl_8_tqs_64.ply,92.85136908 187 | flowerpot_gQP_1_tQP_1.ply,77.9924891 188 | flowerpot_gQP_1_tQP_2.ply,70.82967664 189 | flowerpot_gQP_1_tQP_3.ply,56.33702042 190 | flowerpot_gQP_2_tQP_1.ply,61.47860246 191 | flowerpot_gQP_2_tQP_2.ply,54.35333863 192 | flowerpot_gQP_2_tQP_3.ply,37.97715453 193 | flowerpot_gQP_3_tQP_1.ply,30.83918671 194 | flowerpot_gQP_3_tQP_2.ply,28.31674627 195 | flowerpot_gQP_3_tQP_3.ply,33.17403326 196 | flowerpot_gsigma_0_tsigma_16.ply,62.72356697 197 | flowerpot_gsigma_0_tsigma_32.ply,41.92492394 198 | flowerpot_gsigma_0_tsigma_8.ply,84.32262699 199 | flowerpot_gsigma_2_tsigma_16.ply,45.68188406 200 | flowerpot_gsigma_2_tsigma_32.ply,32.11130646 201 | flowerpot_gsigma_2_tsigma_8.ply,54.12593121 202 | flowerpot_gsigma_4_tsigma_16.ply,41.92953214 203 | flowerpot_gsigma_4_tsigma_32.ply,30.13147384 204 | flowerpot_gsigma_4_tsigma_8.ply,43.83704173 205 | flowerpot_level_7.ply,11.73484953 206 | flowerpot_level_8.ply,17.74699624 207 | flowerpot_level_9.ply,52.62549563 208 | flowerpot_pqs_1_qs_16.ply,91.33270127 209 | flowerpot_pqs_1_qs_32.ply,74.40304247 210 | flowerpot_pqs_1_qs_48.ply,51.57107457 211 | flowerpot_pqs_1_qs_64.ply,27.74273204 212 | flowerpot_tsl_4_tqs_128.ply,4.548071475 213 | flowerpot_tsl_4_tqs_256.ply,9.016393772 214 | flowerpot_tsl_4_tqs_512.ply,9.018618048 215 | flowerpot_tsl_4_tqs_64.ply,8.191728357 216 | flowerpot_tsl_6_tqs_128.ply,61.56948953 217 | flowerpot_tsl_6_tqs_256.ply,38.44130143 218 | flowerpot_tsl_6_tqs_512.ply,24.99414861 219 | flowerpot_tsl_6_tqs_64.ply,72.41894136 220 | flowerpot_tsl_8_tqs_128.ply,76.26218224 221 | flowerpot_tsl_8_tqs_256.ply,50.3957702 222 | flowerpot_tsl_8_tqs_512.ply,31.2096695 223 | flowerpot_tsl_8_tqs_64.ply,91.69369146 224 | glasses_case_gQP_1_tQP_1.ply,79.5227268 225 | glasses_case_gQP_1_tQP_2.ply,75.73406854 226 | glasses_case_gQP_1_tQP_3.ply,52.0035238 227 | glasses_case_gQP_2_tQP_1.ply,60.26589689 228 | glasses_case_gQP_2_tQP_2.ply,56.3976326 229 | glasses_case_gQP_2_tQP_3.ply,41.98232603 230 | glasses_case_gQP_3_tQP_1.ply,27.77453966 231 | glasses_case_gQP_3_tQP_2.ply,29.48230096 232 | glasses_case_gQP_3_tQP_3.ply,28.14483738 233 | glasses_case_gsigma_0_tsigma_16.ply,78.22695922 234 | glasses_case_gsigma_0_tsigma_32.ply,57.46174075 235 | glasses_case_gsigma_0_tsigma_8.ply,89.99496763 236 | glasses_case_gsigma_2_tsigma_16.ply,43.96125739 237 | glasses_case_gsigma_2_tsigma_32.ply,32.33984357 238 | glasses_case_gsigma_2_tsigma_8.ply,42.95205493 239 | glasses_case_gsigma_4_tsigma_16.ply,21.53502251 240 | glasses_case_gsigma_4_tsigma_32.ply,26.68705974 241 | glasses_case_gsigma_4_tsigma_8.ply,22.29518507 242 | glasses_case_level_7.ply,10.70325612 243 | glasses_case_level_8.ply,19.03975259 244 | glasses_case_level_9.ply,69.24813673 245 | glasses_case_pqs_1_qs_16.ply,90.00909084 246 | glasses_case_pqs_1_qs_32.ply,85.17535619 247 | glasses_case_pqs_1_qs_48.ply,69.24420872 248 | glasses_case_pqs_1_qs_64.ply,50.61958082 249 | glasses_case_tsl_4_tqs_128.ply,73.24135477 250 | glasses_case_tsl_4_tqs_256.ply,54.66142653 251 | glasses_case_tsl_4_tqs_512.ply,28.10649938 252 | glasses_case_tsl_4_tqs_64.ply,81.04599946 253 | glasses_case_tsl_6_tqs_128.ply,54.89596744 254 | glasses_case_tsl_6_tqs_256.ply,35.91425495 255 | glasses_case_tsl_6_tqs_512.ply,23.88538209 256 | glasses_case_tsl_6_tqs_64.ply,53.78676569 257 | glasses_case_tsl_8_tqs_128.ply,77.89721037 258 | glasses_case_tsl_8_tqs_256.ply,52.66644107 259 | glasses_case_tsl_8_tqs_512.ply,33.49483616 260 | glasses_case_tsl_8_tqs_64.ply,79.49317365 261 | honeydew_melon_gQP_1_tQP_1.ply,85.52765533 262 | honeydew_melon_gQP_1_tQP_2.ply,74.17353139 263 | honeydew_melon_gQP_1_tQP_3.ply,58.12105658 264 | honeydew_melon_gQP_2_tQP_1.ply,63.27436376 265 | honeydew_melon_gQP_2_tQP_2.ply,62.44898754 266 | honeydew_melon_gQP_2_tQP_3.ply,47.44246097 267 | honeydew_melon_gQP_3_tQP_1.ply,46.08520633 268 | honeydew_melon_gQP_3_tQP_2.ply,45.96369786 269 | honeydew_melon_gQP_3_tQP_3.ply,39.60295452 270 | honeydew_melon_gsigma_0_tsigma_16.ply,79.57471034 271 | honeydew_melon_gsigma_0_tsigma_32.ply,46.84073251 272 | honeydew_melon_gsigma_0_tsigma_8.ply,92.66246004 273 | honeydew_melon_gsigma_2_tsigma_16.ply,54.85708373 274 | honeydew_melon_gsigma_2_tsigma_32.ply,30.28857854 275 | honeydew_melon_gsigma_2_tsigma_8.ply,65.39565957 276 | honeydew_melon_gsigma_4_tsigma_16.ply,44.80889224 277 | honeydew_melon_gsigma_4_tsigma_32.ply,31.01073402 278 | honeydew_melon_gsigma_4_tsigma_8.ply,40.17866933 279 | honeydew_melon_level_7.ply,5.739747099 280 | honeydew_melon_level_8.ply,20.5582786 281 | honeydew_melon_level_9.ply,87.62268458 282 | honeydew_melon_pqs_1_qs_16.ply,88.33736389 283 | honeydew_melon_pqs_1_qs_32.ply,78.17302378 284 | honeydew_melon_pqs_1_qs_48.ply,62.13609637 285 | honeydew_melon_pqs_1_qs_64.ply,30.17348313 286 | honeydew_melon_tsl_4_tqs_128.ply,42.63010541 287 | honeydew_melon_tsl_4_tqs_256.ply,25.91631271 288 | honeydew_melon_tsl_4_tqs_512.ply,10.83065115 289 | honeydew_melon_tsl_4_tqs_64.ply,49.65916531 290 | honeydew_melon_tsl_6_tqs_128.ply,52.89687637 291 | honeydew_melon_tsl_6_tqs_256.ply,35.59875651 292 | honeydew_melon_tsl_6_tqs_512.ply,13.90621355 293 | honeydew_melon_tsl_6_tqs_64.ply,54.8827352 294 | honeydew_melon_tsl_8_tqs_128.ply,81.35073792 295 | honeydew_melon_tsl_8_tqs_256.ply,41.71858667 296 | honeydew_melon_tsl_8_tqs_512.ply,14.29429985 297 | honeydew_melon_tsl_8_tqs_64.ply,88.0734534 298 | house_gQP_1_tQP_1.ply,92.22795336 299 | house_gQP_1_tQP_2.ply,70.65323091 300 | house_gQP_1_tQP_3.ply,38.25820985 301 | house_gQP_2_tQP_1.ply,74.45270788 302 | house_gQP_2_tQP_2.ply,63.06019202 303 | house_gQP_2_tQP_3.ply,40.79004677 304 | house_gQP_3_tQP_1.ply,40.63937374 305 | house_gQP_3_tQP_2.ply,37.12616089 306 | house_gQP_3_tQP_3.ply,29.22124359 307 | house_gsigma_0_tsigma_16.ply,69.80583942 308 | house_gsigma_0_tsigma_32.ply,48.93321579 309 | house_gsigma_0_tsigma_8.ply,90.17113564 310 | house_gsigma_2_tsigma_16.ply,46.30083599 311 | house_gsigma_2_tsigma_32.ply,34.86645876 312 | house_gsigma_2_tsigma_8.ply,48.68141211 313 | house_gsigma_4_tsigma_16.ply,29.89754144 314 | house_gsigma_4_tsigma_32.ply,19.38454229 315 | house_gsigma_4_tsigma_8.ply,35.68854777 316 | house_level_7.ply,2.953877424 317 | house_level_8.ply,15.94446242 318 | house_level_9.ply,63.69121032 319 | house_pqs_1_qs_16.ply,88.432011 320 | house_pqs_1_qs_32.ply,77.69216307 321 | house_pqs_1_qs_48.ply,68.06703004 322 | house_pqs_1_qs_64.ply,50.59385382 323 | house_tsl_4_tqs_128.ply,54.31101695 324 | house_tsl_4_tqs_256.ply,37.90143461 325 | house_tsl_4_tqs_512.ply,22.39643651 326 | house_tsl_4_tqs_64.ply,65.09485808 327 | house_tsl_6_tqs_128.ply,75.3571452 328 | house_tsl_6_tqs_256.ply,42.98648413 329 | house_tsl_6_tqs_512.ply,26.58473454 330 | house_tsl_6_tqs_64.ply,84.77302695 331 | house_tsl_8_tqs_128.ply,80.24781187 332 | house_tsl_8_tqs_256.ply,52.60120652 333 | house_tsl_8_tqs_512.ply,27.60330842 334 | house_tsl_8_tqs_64.ply,86.7884343 335 | litchi_gQP_1_tQP_1.ply,83.82459716 336 | litchi_gQP_1_tQP_2.ply,73.7164483 337 | litchi_gQP_1_tQP_3.ply,59.29219253 338 | litchi_gQP_2_tQP_1.ply,62.08415891 339 | litchi_gQP_2_tQP_2.ply,49.46809496 340 | litchi_gQP_2_tQP_3.ply,40.8373808 341 | litchi_gQP_3_tQP_1.ply,38.8157313 342 | litchi_gQP_3_tQP_2.ply,41.62178436 343 | litchi_gQP_3_tQP_3.ply,29.95124476 344 | litchi_gsigma_0_tsigma_16.ply,78.99324726 345 | litchi_gsigma_0_tsigma_32.ply,40.75832148 346 | litchi_gsigma_0_tsigma_8.ply,92.5519659 347 | litchi_gsigma_2_tsigma_16.ply,55.46141956 348 | litchi_gsigma_2_tsigma_32.ply,39.75588339 349 | litchi_gsigma_2_tsigma_8.ply,66.69196192 350 | litchi_gsigma_4_tsigma_16.ply,43.39649489 351 | litchi_gsigma_4_tsigma_32.ply,32.56601573 352 | litchi_gsigma_4_tsigma_8.ply,39.07952026 353 | litchi_level_7.ply,2.482748258 354 | litchi_level_8.ply,23.51904767 355 | litchi_level_9.ply,88.25654022 356 | litchi_pqs_1_qs_16.ply,90.59614947 357 | litchi_pqs_1_qs_32.ply,72.26469753 358 | litchi_pqs_1_qs_48.ply,43.15755648 359 | litchi_pqs_1_qs_64.ply,32.09673457 360 | litchi_tsl_4_tqs_128.ply,33.92753037 361 | litchi_tsl_4_tqs_256.ply,24.48177139 362 | litchi_tsl_4_tqs_512.ply,14.16383462 363 | litchi_tsl_4_tqs_64.ply,41.44995622 364 | litchi_tsl_6_tqs_128.ply,66.52326242 365 | litchi_tsl_6_tqs_256.ply,38.63096165 366 | litchi_tsl_6_tqs_512.ply,21.54700544 367 | litchi_tsl_6_tqs_64.ply,76.61593988 368 | litchi_tsl_8_tqs_128.ply,72.29047685 369 | litchi_tsl_8_tqs_256.ply,52.24182148 370 | litchi_tsl_8_tqs_512.ply,25.86577269 371 | litchi_tsl_8_tqs_64.ply,89.13485285 372 | mushroom_gQP_1_tQP_1.ply,77.73240747 373 | mushroom_gQP_1_tQP_2.ply,43.61266042 374 | mushroom_gQP_1_tQP_3.ply,36.81092275 375 | mushroom_gQP_2_tQP_1.ply,60.55695582 376 | mushroom_gQP_2_tQP_2.ply,46.37013719 377 | mushroom_gQP_2_tQP_3.ply,30.96539789 378 | mushroom_gQP_3_tQP_1.ply,30.04520696 379 | mushroom_gQP_3_tQP_2.ply,26.24890703 380 | mushroom_gQP_3_tQP_3.ply,20.3048466 381 | mushroom_gsigma_0_tsigma_16.ply,75.09878823 382 | mushroom_gsigma_0_tsigma_32.ply,49.35994386 383 | mushroom_gsigma_0_tsigma_8.ply,94.30498452 384 | mushroom_gsigma_2_tsigma_16.ply,46.54985717 385 | mushroom_gsigma_2_tsigma_32.ply,38.48644541 386 | mushroom_gsigma_2_tsigma_8.ply,53.853365 387 | mushroom_gsigma_4_tsigma_16.ply,37.94959663 388 | mushroom_gsigma_4_tsigma_32.ply,25.0118684 389 | mushroom_gsigma_4_tsigma_8.ply,43.44282972 390 | mushroom_level_7.ply,3.822951858 391 | mushroom_level_8.ply,14.84418465 392 | mushroom_level_9.ply,60.86093383 393 | mushroom_pqs_1_qs_16.ply,89.21731077 394 | mushroom_pqs_1_qs_32.ply,67.34013651 395 | mushroom_pqs_1_qs_48.ply,44.95037706 396 | mushroom_pqs_1_qs_64.ply,36.25293665 397 | mushroom_tsl_4_tqs_128.ply,25.11673039 398 | mushroom_tsl_4_tqs_256.ply,23.09139883 399 | mushroom_tsl_4_tqs_512.ply,15.51013445 400 | mushroom_tsl_4_tqs_64.ply,30.03413945 401 | mushroom_tsl_6_tqs_128.ply,48.06095734 402 | mushroom_tsl_6_tqs_256.ply,31.50071671 403 | mushroom_tsl_6_tqs_512.ply,19.43317801 404 | mushroom_tsl_6_tqs_64.ply,67.31096329 405 | mushroom_tsl_8_tqs_128.ply,67.67772932 406 | mushroom_tsl_8_tqs_256.ply,40.49803148 407 | mushroom_tsl_8_tqs_512.ply,25.02467546 408 | mushroom_tsl_8_tqs_64.ply,88.27427314 409 | pen_container_gQP_1_tQP_1.ply,73.52634804 410 | pen_container_gQP_1_tQP_2.ply,70.28884535 411 | pen_container_gQP_1_tQP_3.ply,46.53720777 412 | pen_container_gQP_2_tQP_1.ply,53.34469001 413 | pen_container_gQP_2_tQP_2.ply,44.33274307 414 | pen_container_gQP_2_tQP_3.ply,48.20411042 415 | pen_container_gQP_3_tQP_1.ply,33.30402412 416 | pen_container_gQP_3_tQP_2.ply,41.84397306 417 | pen_container_gQP_3_tQP_3.ply,31.29669104 418 | pen_container_gsigma_0_tsigma_16.ply,71.56783481 419 | pen_container_gsigma_0_tsigma_32.ply,46.96382835 420 | pen_container_gsigma_0_tsigma_8.ply,89.21398884 421 | pen_container_gsigma_2_tsigma_16.ply,42.52148539 422 | pen_container_gsigma_2_tsigma_32.ply,31.71810296 423 | pen_container_gsigma_2_tsigma_8.ply,49.60595949 424 | pen_container_gsigma_4_tsigma_16.ply,27.73032223 425 | pen_container_gsigma_4_tsigma_32.ply,26.76427729 426 | pen_container_gsigma_4_tsigma_8.ply,32.68372466 427 | pen_container_level_7.ply,2.804935525 428 | pen_container_level_8.ply,21.11616176 429 | pen_container_level_9.ply,69.83589159 430 | pen_container_pqs_1_qs_16.ply,89.99880847 431 | pen_container_pqs_1_qs_32.ply,76.86578198 432 | pen_container_pqs_1_qs_48.ply,65.20220124 433 | pen_container_pqs_1_qs_64.ply,54.48224583 434 | pen_container_tsl_4_tqs_128.ply,16.44390869 435 | pen_container_tsl_4_tqs_256.ply,15.93659647 436 | pen_container_tsl_4_tqs_512.ply,12.59679012 437 | pen_container_tsl_4_tqs_64.ply,16.4123228 438 | pen_container_tsl_6_tqs_128.ply,66.92772188 439 | pen_container_tsl_6_tqs_256.ply,41.38784241 440 | pen_container_tsl_6_tqs_512.ply,25.2678055 441 | pen_container_tsl_6_tqs_64.ply,81.92204441 442 | pen_container_tsl_8_tqs_128.ply,77.20825479 443 | pen_container_tsl_8_tqs_256.ply,56.30186956 444 | pen_container_tsl_8_tqs_512.ply,33.14918481 445 | pen_container_tsl_8_tqs_64.ply,85.34021252 446 | pineapple_gQP_1_tQP_1.ply,80.61949084 447 | pineapple_gQP_1_tQP_2.ply,63.4549413 448 | pineapple_gQP_1_tQP_3.ply,46.0406663 449 | pineapple_gQP_2_tQP_1.ply,65.78228073 450 | pineapple_gQP_2_tQP_2.ply,50.05562483 451 | pineapple_gQP_2_tQP_3.ply,34.38091608 452 | pineapple_gQP_3_tQP_1.ply,49.76449396 453 | pineapple_gQP_3_tQP_2.ply,48.87305811 454 | pineapple_gQP_3_tQP_3.ply,32.0423144 455 | pineapple_gsigma_0_tsigma_16.ply,71.00193693 456 | pineapple_gsigma_0_tsigma_32.ply,46.79505656 457 | pineapple_gsigma_0_tsigma_8.ply,84.52102377 458 | pineapple_gsigma_2_tsigma_16.ply,46.57022418 459 | pineapple_gsigma_2_tsigma_32.ply,33.10576109 460 | pineapple_gsigma_2_tsigma_8.ply,54.34960733 461 | pineapple_gsigma_4_tsigma_16.ply,37.64440405 462 | pineapple_gsigma_4_tsigma_32.ply,32.97754017 463 | pineapple_gsigma_4_tsigma_8.ply,39.16435793 464 | pineapple_level_7.ply,2.239905326 465 | pineapple_level_8.ply,19.29522572 466 | pineapple_level_9.ply,68.22419612 467 | pineapple_pqs_1_qs_16.ply,87.77451825 468 | pineapple_pqs_1_qs_32.ply,68.39850184 469 | pineapple_pqs_1_qs_48.ply,39.87160473 470 | pineapple_pqs_1_qs_64.ply,23.88089977 471 | pineapple_tsl_4_tqs_128.ply,63.26745992 472 | pineapple_tsl_4_tqs_256.ply,38.95716868 473 | pineapple_tsl_4_tqs_512.ply,22.17458195 474 | pineapple_tsl_4_tqs_64.ply,72.74186109 475 | pineapple_tsl_6_tqs_128.ply,54.87963725 476 | pineapple_tsl_6_tqs_256.ply,42.95864394 477 | pineapple_tsl_6_tqs_512.ply,22.95752369 478 | pineapple_tsl_6_tqs_64.ply,63.04691124 479 | pineapple_tsl_8_tqs_128.ply,74.12073085 480 | pineapple_tsl_8_tqs_256.ply,49.59547029 481 | pineapple_tsl_8_tqs_512.ply,30.34832817 482 | pineapple_tsl_8_tqs_64.ply,86.21409658 483 | ping-pong_bat_gQP_1_tQP_1.ply,73.32501116 484 | ping-pong_bat_gQP_1_tQP_2.ply,63.85195787 485 | ping-pong_bat_gQP_1_tQP_3.ply,53.26474741 486 | ping-pong_bat_gQP_2_tQP_1.ply,61.7305609 487 | ping-pong_bat_gQP_2_tQP_2.ply,57.05151055 488 | ping-pong_bat_gQP_2_tQP_3.ply,42.26146311 489 | ping-pong_bat_gQP_3_tQP_1.ply,35.55000273 490 | ping-pong_bat_gQP_3_tQP_2.ply,34.66209677 491 | ping-pong_bat_gQP_3_tQP_3.ply,36.75484397 492 | ping-pong_bat_gsigma_0_tsigma_16.ply,59.9268872 493 | ping-pong_bat_gsigma_0_tsigma_32.ply,45.26451967 494 | ping-pong_bat_gsigma_0_tsigma_8.ply,85.76939372 495 | ping-pong_bat_gsigma_2_tsigma_16.ply,43.72573164 496 | ping-pong_bat_gsigma_2_tsigma_32.ply,36.8020878 497 | ping-pong_bat_gsigma_2_tsigma_8.ply,53.59356464 498 | ping-pong_bat_gsigma_4_tsigma_16.ply,31.37290037 499 | ping-pong_bat_gsigma_4_tsigma_32.ply,28.12785458 500 | ping-pong_bat_gsigma_4_tsigma_8.ply,36.83180396 501 | ping-pong_bat_level_7.ply,10.66904823 502 | ping-pong_bat_level_8.ply,26.11003906 503 | ping-pong_bat_level_9.ply,85.71748306 504 | ping-pong_bat_pqs_1_qs_16.ply,91.84153463 505 | ping-pong_bat_pqs_1_qs_32.ply,57.33546331 506 | ping-pong_bat_pqs_1_qs_48.ply,41.32431727 507 | ping-pong_bat_pqs_1_qs_64.ply,28.34999248 508 | ping-pong_bat_tsl_4_tqs_128.ply,5.608914607 509 | ping-pong_bat_tsl_4_tqs_256.ply,4.172555657 510 | ping-pong_bat_tsl_4_tqs_512.ply,5.085960367 511 | ping-pong_bat_tsl_4_tqs_64.ply,5.51761495 512 | ping-pong_bat_tsl_6_tqs_128.ply,50.0484513 513 | ping-pong_bat_tsl_6_tqs_256.ply,33.49455035 514 | ping-pong_bat_tsl_6_tqs_512.ply,26.98407872 515 | ping-pong_bat_tsl_6_tqs_64.ply,70.2438173 516 | ping-pong_bat_tsl_8_tqs_128.ply,52.63107514 517 | ping-pong_bat_tsl_8_tqs_256.ply,38.25323224 518 | ping-pong_bat_tsl_8_tqs_512.ply,30.36277636 519 | ping-pong_bat_tsl_8_tqs_64.ply,77.24077289 520 | puer_tea_gQP_1_tQP_1.ply,77.91470095 521 | puer_tea_gQP_1_tQP_2.ply,75.4444591 522 | puer_tea_gQP_1_tQP_3.ply,51.29887452 523 | puer_tea_gQP_2_tQP_1.ply,62.50545414 524 | puer_tea_gQP_2_tQP_2.ply,63.42565477 525 | puer_tea_gQP_2_tQP_3.ply,48.99328461 526 | puer_tea_gQP_3_tQP_1.ply,49.25330318 527 | puer_tea_gQP_3_tQP_2.ply,42.08416878 528 | puer_tea_gQP_3_tQP_3.ply,37.31071354 529 | puer_tea_gsigma_0_tsigma_16.ply,48.63259403 530 | puer_tea_gsigma_0_tsigma_32.ply,34.7408577 531 | puer_tea_gsigma_0_tsigma_8.ply,79.47669335 532 | puer_tea_gsigma_2_tsigma_16.ply,32.5523098 533 | puer_tea_gsigma_2_tsigma_32.ply,27.4135991 534 | puer_tea_gsigma_2_tsigma_8.ply,43.47026556 535 | puer_tea_gsigma_4_tsigma_16.ply,22.98694951 536 | puer_tea_gsigma_4_tsigma_32.ply,17.4081868 537 | puer_tea_gsigma_4_tsigma_8.ply,24.00198769 538 | puer_tea_level_7.ply,3.153462529 539 | puer_tea_level_8.ply,16.26033493 540 | puer_tea_level_9.ply,77.84326641 541 | puer_tea_pqs_1_qs_16.ply,88.77922488 542 | puer_tea_pqs_1_qs_32.ply,57.83074034 543 | puer_tea_pqs_1_qs_48.ply,53.83101443 544 | puer_tea_pqs_1_qs_64.ply,48.01354497 545 | puer_tea_tsl_4_tqs_128.ply,26.81991353 546 | puer_tea_tsl_4_tqs_256.ply,12.19981534 547 | puer_tea_tsl_4_tqs_512.ply,5.842224721 548 | puer_tea_tsl_4_tqs_64.ply,22.63056663 549 | puer_tea_tsl_6_tqs_128.ply,62.98621215 550 | puer_tea_tsl_6_tqs_256.ply,28.55586178 551 | puer_tea_tsl_6_tqs_512.ply,16.59954847 552 | puer_tea_tsl_6_tqs_64.ply,86.88044877 553 | puer_tea_tsl_8_tqs_128.ply,61.79745668 554 | puer_tea_tsl_8_tqs_256.ply,36.11364107 555 | puer_tea_tsl_8_tqs_512.ply,21.54590932 556 | puer_tea_tsl_8_tqs_64.ply,79.84533052 557 | pumpkin_gQP_1_tQP_1.ply,80.03200907 558 | pumpkin_gQP_1_tQP_2.ply,63.62860455 559 | pumpkin_gQP_1_tQP_3.ply,41.50382095 560 | pumpkin_gQP_2_tQP_1.ply,57.30854338 561 | pumpkin_gQP_2_tQP_2.ply,56.34774299 562 | pumpkin_gQP_2_tQP_3.ply,41.51596423 563 | pumpkin_gQP_3_tQP_1.ply,46.14227709 564 | pumpkin_gQP_3_tQP_2.ply,37.54247859 565 | pumpkin_gQP_3_tQP_3.ply,30.23205216 566 | pumpkin_gsigma_0_tsigma_16.ply,73.8480628 567 | pumpkin_gsigma_0_tsigma_32.ply,47.64524874 568 | pumpkin_gsigma_0_tsigma_8.ply,88.71289491 569 | pumpkin_gsigma_2_tsigma_16.ply,50.88715487 570 | pumpkin_gsigma_2_tsigma_32.ply,36.13580208 571 | pumpkin_gsigma_2_tsigma_8.ply,59.70275662 572 | pumpkin_gsigma_4_tsigma_16.ply,37.90024828 573 | pumpkin_gsigma_4_tsigma_32.ply,26.06866845 574 | pumpkin_gsigma_4_tsigma_8.ply,37.62442881 575 | pumpkin_level_7.ply,1.812600251 576 | pumpkin_level_8.ply,20.6394442 577 | pumpkin_level_9.ply,76.03000232 578 | pumpkin_pqs_1_qs_16.ply,85.62141081 579 | pumpkin_pqs_1_qs_32.ply,83.04295679 580 | pumpkin_pqs_1_qs_48.ply,71.63365401 581 | pumpkin_pqs_1_qs_64.ply,52.24143669 582 | pumpkin_tsl_4_tqs_128.ply,78.67404727 583 | pumpkin_tsl_4_tqs_256.ply,53.12513301 584 | pumpkin_tsl_4_tqs_512.ply,28.67861771 585 | pumpkin_tsl_4_tqs_64.ply,86.44648425 586 | pumpkin_tsl_6_tqs_128.ply,62.2123483 587 | pumpkin_tsl_6_tqs_256.ply,48.3776982 588 | pumpkin_tsl_6_tqs_512.ply,25.68112045 589 | pumpkin_tsl_6_tqs_64.ply,66.90624806 590 | pumpkin_tsl_8_tqs_128.ply,78.97698177 591 | pumpkin_tsl_8_tqs_256.ply,55.34363898 592 | pumpkin_tsl_8_tqs_512.ply,29.76480834 593 | pumpkin_tsl_8_tqs_64.ply,87.77156724 594 | ship_gQP_1_tQP_1.ply,86.83243163 595 | ship_gQP_1_tQP_2.ply,79.66953395 596 | ship_gQP_1_tQP_3.ply,64.65363881 597 | ship_gQP_2_tQP_1.ply,52.75587535 598 | ship_gQP_2_tQP_2.ply,53.07130936 599 | ship_gQP_2_tQP_3.ply,44.99481835 600 | ship_gQP_3_tQP_1.ply,32.70113044 601 | ship_gQP_3_tQP_2.ply,30.07825606 602 | ship_gQP_3_tQP_3.ply,32.37187738 603 | ship_gsigma_0_tsigma_16.ply,60.02590379 604 | ship_gsigma_0_tsigma_32.ply,44.71311782 605 | ship_gsigma_0_tsigma_8.ply,81.87614964 606 | ship_gsigma_2_tsigma_16.ply,46.24280768 607 | ship_gsigma_2_tsigma_32.ply,29.47047405 608 | ship_gsigma_2_tsigma_8.ply,44.32391178 609 | ship_gsigma_4_tsigma_16.ply,30.23841126 610 | ship_gsigma_4_tsigma_32.ply,22.57182481 611 | ship_gsigma_4_tsigma_8.ply,24.15903763 612 | ship_level_7.ply,5.790659407 613 | ship_level_8.ply,15.26715745 614 | ship_level_9.ply,51.16749334 615 | ship_pqs_1_qs_16.ply,91.89237886 616 | ship_pqs_1_qs_32.ply,68.90628136 617 | ship_pqs_1_qs_48.ply,60.85370266 618 | ship_pqs_1_qs_64.ply,48.18439311 619 | ship_tsl_4_tqs_128.ply,15.97910476 620 | ship_tsl_4_tqs_256.ply,11.72465248 621 | ship_tsl_4_tqs_512.ply,12.63710724 622 | ship_tsl_4_tqs_64.ply,13.43235303 623 | ship_tsl_6_tqs_128.ply,68.10486694 624 | ship_tsl_6_tqs_256.ply,41.18616309 625 | ship_tsl_6_tqs_512.ply,25.1244404 626 | ship_tsl_6_tqs_64.ply,80.47808166 627 | ship_tsl_8_tqs_128.ply,76.5494553 628 | ship_tsl_8_tqs_256.ply,45.60453851 629 | ship_tsl_8_tqs_512.ply,32.56767817 630 | ship_tsl_8_tqs_64.ply,86.41452606 631 | statue_gQP_1_tQP_1.ply,82.16544475 632 | statue_gQP_1_tQP_2.ply,74.36868381 633 | statue_gQP_1_tQP_3.ply,52.98086999 634 | statue_gQP_2_tQP_1.ply,65.277898 635 | statue_gQP_2_tQP_2.ply,58.03915102 636 | statue_gQP_2_tQP_3.ply,45.29396388 637 | statue_gQP_3_tQP_1.ply,25.88641621 638 | statue_gQP_3_tQP_2.ply,32.58541188 639 | statue_gQP_3_tQP_3.ply,26.9301373 640 | statue_gsigma_0_tsigma_16.ply,69.24903345 641 | statue_gsigma_0_tsigma_32.ply,49.2759668 642 | statue_gsigma_0_tsigma_8.ply,85.11947303 643 | statue_gsigma_2_tsigma_16.ply,39.41233042 644 | statue_gsigma_2_tsigma_32.ply,34.7558873 645 | statue_gsigma_2_tsigma_8.ply,50.14553046 646 | statue_gsigma_4_tsigma_16.ply,27.72288456 647 | statue_gsigma_4_tsigma_32.ply,32.99521449 648 | statue_gsigma_4_tsigma_8.ply,31.38009148 649 | statue_level_7.ply,11.04592674 650 | statue_level_8.ply,24.44605069 651 | statue_level_9.ply,69.02141066 652 | statue_pqs_1_qs_16.ply,87.01573408 653 | statue_pqs_1_qs_32.ply,77.27978223 654 | statue_pqs_1_qs_48.ply,67.40476035 655 | statue_pqs_1_qs_64.ply,54.10887739 656 | statue_tsl_4_tqs_128.ply,19.6972695 657 | statue_tsl_4_tqs_256.ply,16.40082892 658 | statue_tsl_4_tqs_512.ply,11.34401742 659 | statue_tsl_4_tqs_64.ply,19.55881932 660 | statue_tsl_6_tqs_128.ply,67.76035361 661 | statue_tsl_6_tqs_256.ply,48.22983919 662 | statue_tsl_6_tqs_512.ply,29.73847696 663 | statue_tsl_6_tqs_64.ply,77.30130872 664 | statue_tsl_8_tqs_128.ply,78.36511751 665 | statue_tsl_8_tqs_256.ply,57.66573011 666 | statue_tsl_8_tqs_512.ply,35.57328452 667 | statue_tsl_8_tqs_64.ply,88.31888572 668 | stone_gQP_1_tQP_1.ply,78.81994767 669 | stone_gQP_1_tQP_2.ply,55.96429471 670 | stone_gQP_1_tQP_3.ply,38.19125206 671 | stone_gQP_2_tQP_1.ply,58.87870931 672 | stone_gQP_2_tQP_2.ply,56.0219352 673 | stone_gQP_2_tQP_3.ply,34.46225059 674 | stone_gQP_3_tQP_1.ply,41.76109598 675 | stone_gQP_3_tQP_2.ply,35.62049168 676 | stone_gQP_3_tQP_3.ply,26.01342874 677 | stone_gsigma_0_tsigma_16.ply,75.06155601 678 | stone_gsigma_0_tsigma_32.ply,47.83686368 679 | stone_gsigma_0_tsigma_8.ply,86.87668482 680 | stone_gsigma_2_tsigma_16.ply,62.4059409 681 | stone_gsigma_2_tsigma_32.ply,47.62512297 682 | stone_gsigma_2_tsigma_8.ply,71.61570912 683 | stone_gsigma_4_tsigma_16.ply,36.85294226 684 | stone_gsigma_4_tsigma_32.ply,24.09074637 685 | stone_gsigma_4_tsigma_8.ply,34.41591904 686 | stone_level_7.ply,8.580533193 687 | stone_level_8.ply,20.64931138 688 | stone_level_9.ply,86.87232128 689 | stone_pqs_1_qs_16.ply,80.90892544 690 | stone_pqs_1_qs_32.ply,69.48493262 691 | stone_pqs_1_qs_48.ply,77.06861834 692 | stone_pqs_1_qs_64.ply,65.57957273 693 | stone_tsl_4_tqs_128.ply,47.17354673 694 | stone_tsl_4_tqs_256.ply,34.22061434 695 | stone_tsl_4_tqs_512.ply,18.76360461 696 | stone_tsl_4_tqs_64.ply,57.63837411 697 | stone_tsl_6_tqs_128.ply,60.65492093 698 | stone_tsl_6_tqs_256.ply,42.3730925 699 | stone_tsl_6_tqs_512.ply,22.63591963 700 | stone_tsl_6_tqs_64.ply,65.93925975 701 | stone_tsl_8_tqs_128.ply,75.68153655 702 | stone_tsl_8_tqs_256.ply,47.43694567 703 | stone_tsl_8_tqs_512.ply,20.31259474 704 | stone_tsl_8_tqs_64.ply,76.96861244 705 | tool_box_gQP_1_tQP_1.ply,77.18180348 706 | tool_box_gQP_1_tQP_2.ply,59.17937263 707 | tool_box_gQP_1_tQP_3.ply,56.75944471 708 | tool_box_gQP_2_tQP_1.ply,57.87079799 709 | tool_box_gQP_2_tQP_2.ply,57.21173403 710 | tool_box_gQP_2_tQP_3.ply,45.32187464 711 | tool_box_gQP_3_tQP_1.ply,44.48994269 712 | tool_box_gQP_3_tQP_2.ply,47.63715478 713 | tool_box_gQP_3_tQP_3.ply,34.6512822 714 | tool_box_gsigma_0_tsigma_16.ply,47.65395833 715 | tool_box_gsigma_0_tsigma_32.ply,33.38089522 716 | tool_box_gsigma_0_tsigma_8.ply,74.07774205 717 | tool_box_gsigma_2_tsigma_16.ply,30.49113539 718 | tool_box_gsigma_2_tsigma_32.ply,19.73538966 719 | tool_box_gsigma_2_tsigma_8.ply,42.15868369 720 | tool_box_gsigma_4_tsigma_16.ply,22.03286169 721 | tool_box_gsigma_4_tsigma_32.ply,13.71265605 722 | tool_box_gsigma_4_tsigma_8.ply,27.6145146 723 | tool_box_level_7.ply,7.788150814 724 | tool_box_level_8.ply,25.35706923 725 | tool_box_level_9.ply,72.96806866 726 | tool_box_pqs_1_qs_16.ply,82.50891381 727 | tool_box_pqs_1_qs_32.ply,58.74124972 728 | tool_box_pqs_1_qs_48.ply,38.80305363 729 | tool_box_pqs_1_qs_64.ply,22.59810553 730 | tool_box_tsl_4_tqs_128.ply,43.48063326 731 | tool_box_tsl_4_tqs_256.ply,21.33990424 732 | tool_box_tsl_4_tqs_512.ply,9.772409239 733 | tool_box_tsl_4_tqs_64.ply,53.81005804 734 | tool_box_tsl_6_tqs_128.ply,46.71853642 735 | tool_box_tsl_6_tqs_256.ply,31.69048484 736 | tool_box_tsl_6_tqs_512.ply,13.24459477 737 | tool_box_tsl_6_tqs_64.ply,80.4800722 738 | tool_box_tsl_8_tqs_128.ply,52.16919467 739 | tool_box_tsl_8_tqs_256.ply,29.3246156 740 | tool_box_tsl_8_tqs_512.ply,18.08086476 741 | tool_box_tsl_8_tqs_64.ply,73.87057596 742 | --------------------------------------------------------------------------------