├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Jasmine-tjy 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 | # Semantic-based-Point-Cloud-Tasks 2 | This is an open-source repository for semantic based point cloud tasks, and we aim to provide a comprehensive summary of various semantic based point cloud tasks. The paper ***[Implicit Guidance and Explicit Representation of Semantic Information in Points Cloud: A Survey](https://arxiv.org/abs/2501.05473)*** corresponding to this repository provides a detailed introduction and analysis of the application of semantic information in both traditional and emerging tasks in point clouds. If you find our work useful in your research, please consider citing: 3 | ``` 4 | @misc{tang2025implicitguidanceexplicitrepresentation, 5 | title={Implicit Guidance and Explicit Representation of Semantic Information in Points Cloud: A Survey}, 6 | author={Jingyuan Tang and Yuhuan Zhao and Songlin Sun and Yangang Cai}, 7 | year={2025}, 8 | eprint={2501.05473}, 9 | archivePrefix={arXiv}, 10 | primaryClass={cs.CV}, 11 | url={https://arxiv.org/abs/2501.05473}, 12 | } 13 | ``` 14 | 15 | ## 1. Traditional Point Cloud Tasks with Semantic :diamond_shape_with_a_dot_inside: 16 | ### 1.1 Point Cloud Semantic Segmentation :small_blue_diamond: 17 | #### 1.1.1 Top-down Segmentation Method 18 | * [SOLOv2: Dynamic and Fast Instance Segmentation](https://neurips.cc/virtual/2020/public/poster_cd3afef9b8b89558cd56638c3631868a.html) \[2020 NIPS\] :octocat:[code](https://git.io/AdelaiDet) 19 | * [SOLO: A Simple Framework for Instance Segmentation](https://ieeexplore.ieee.org/document/9536421) \[2022 TPAMI\] :octocat:[code](https://git.io/AdelaiDet) 20 | * [NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds](https://arxiv.org/pdf/2207.09978v1) \[2023 WACV\] 21 | 22 | #### 1.1.2 Bottom-up Semantic Segmentation Method 23 | ##### (1) Voxel-based Semantic Segmentation 24 | * [Pointgrid: A deep network for 3d shape understanding](https://ieeexplore.ieee.org/document/8579057) \[2018 CVPR\] :octocat:[code](https://github.com/trucleduc/PointGrid) 25 | * [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://www.semanticscholar.org/paper/Searching-Efficient-3D-Architectures-with-Sparse/769dbcb80cc1d2d17ae7c524644103d0f6595452) \[2020 ECCV\] :octocat:[code](https://github.com/mit-han-lab/spvnas) 26 | * [VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation](https://ieeexplore.ieee.org/document/9710530) \[2021 ICCV\] :octocat:[code](https://github.com/hzykent/VMNet) 27 | * [GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds](https://arxiv.org/pdf/2403.19220) \[2024 CVPR\] :octocat: [code](https://github.com/zhangshengjun2019/GeoAuxNet) 28 | * [PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation](https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_PanoOcc_Unified_Occupancy_Representation_for_Camera-based_3D_Panoptic_Segmentation_CVPR_2024_paper.pdf) \[2024 CVPR\] :octocat: [code](https://github.com/Robertwyq/PanoOcc) 29 | 30 | ##### (2) Projection-based Semantic Segmentation 31 | * [Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud](https://ieeexplore.ieee.org/document/8462926) \[2018 ICRA\] :octocat:[code](https://github.com/BichenWuUCB/SqueezeSeg) 32 | * [Rangenet++: Fast and accurate lidar semantic segmentation](https://ieeexplore.ieee.org/document/8967762) \[2019 IROS\] :octocat:[code](https://github.com/PRBonn/lidar-bonnetal) 33 | * [Squeezesegv2: Improved model structure and unsupervised domain adaptation for roadobject segmentation from a lidar point cloud](https://ieeexplore.ieee.org/abstract/document/8793495) \[2019 ICRA\] :octocat:[code](https://github.com/xuanyuzhou98/SqueezeSegV2) 34 | * [Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds](https://link.springer.com/content/pdf/10.1007/978-3-030-64559-5_16) \[2020 ISVC\] :octocat:[code](https://github.com/TiagoCortinhal/SalsaNext) 35 | * [Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation](https://link.springer.com/chapter/10.1007/978-3-030-58604-1_1) \[2020 ECCV\] :octocat:[code](https://github.com/chenfengxu714/SqueezeSegV3) 36 | * [Active learning based 3d semantic labeling from images and videos](https://ieeexplore.ieee.org/document/9430559) \[2022 TCSVT\] 37 | * [Efficient 3d scene semantic segmentation via active learning on rendered 2d images](https://ieeexplore.ieee.org/document/10158507) \[2023 TIP\] 38 | * [3d semantic segmentation of aerial photogrammetry models based on orthographic projection](https://ieeexplore.ieee.org/document/10119167) \[2023 TCSVT\] 39 | * [Knowledge distillation from 3d to bird’s-eye-view for lidar semantic segmentation](https://ieeexplore.ieee.org/document/10220057) \[2023 ICME\] :octocat:[code](https://github.com/fengjiang5/Knowledge-Distillation-from-Cylinder3D-to-PolarNet) 40 | * [Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving](https://ieeexplore.ieee.org/document/10204428) \[2023 CVPR\] :octocat:[code](https://github.com/valeoai/rangevit) 41 | * [Bird's-Eye-View Semantic Segmentation With Two-Stream Compact Depth Transformation and Feature Rectification](https://ieeexplore.ieee.org/abstract/document/10124335) \[2023 TIV\] 42 | * [Residual graph convolutional network for bird’seye-view semantic segmentation](https://ieeexplore.ieee.org/document/10483624) \[2024 WACV\] 43 | 44 | ##### (3) Point-based Semantic Segmentation 45 | * [Pointnet++: Deep hierarchical feature learning on point sets in a metric space](https://dl.acm.org/doi/abs/10.5555/3295222.3295263) \[2017 NIPS\] :octocat:[code](https://github.com/charlesq34/pointnet2) 46 | * [Point transformer](https://ieeexplore.ieee.org/document/9710703) \[2021 ICCV\] 47 | * [Backward attentive fusing network with local aggregation classifier for 3d point cloud semantic segmentation](https://ieeexplore.ieee.org/abstract/document/9410334) \[2021 TIP\] :octocat:[code](https://github.com/Xiangxu-0103/BAF-LAC) 48 | * [Cga-net: Category guided aggregation for point cloud semantic segmentation](https://ieeexplore.ieee.org/document/9577467) \[2021 CVPR\] :octocat:[code](https://github.com/MCG-NJU/CGA-Net) 49 | * [Point transformer v2: Grouped vector attention and partition-based pooling](https://papers.nips.cc/paper_files/paper/2022/hash/d78ece6613953f46501b958b7bb4582f-Abstract-Conference.html) \[2022 NIPS\] :octocat:[code](https://github.com/Gofinge/PointTransformerV2) 50 | * [Dcnet: Large-scale point cloud semantic segmentation with discriminative and efficient feature aggregation](https://ieeexplore.ieee.org/document/10025770) \[2023 TCSVT\] :octocat:[code](https://github.com/fukunyin/DCNet) 51 | * [Point transformer v3: Simpler, faster, stronger](https://arxiv.org/abs/2312.10035) \[2024 CVPR\] :octocat:[code](https://github.com/Pointcept/PointTransformerV3) 52 | * [KPConvX: Modernizing Kernel Point Convolution with Kernel Attention](https://openaccess.thecvf.com/content/CVPR2024/html/Thomas_KPConvX_Modernizing_Kernel_Point_Convolution_with_Kernel_Attention_CVPR_2024_paper.html) \[2024 CVPR\] :octocat:[code](https://github.com/apple/ml-kpconvx) 53 | 54 | ##### (4) Unit-sets-based on Semantic Segmentation 55 | * [Instance segmentation in 3d scenes using semantic superpoint tree networks](https://ieeexplore.ieee.org/document/9709996) \[2021 ICCV\] :octocat:[code](https://github.com/Gorilla-Lab-SCUT/SSTNet) 56 | * [Sspc-net: Semi-supervised semantic 3d point cloud segmentation network](https://ojs.aaai.org/index.php/AAAI/article/view/16200) \[2021 AAAI\] :octocat:[code](https://github.com/MMCheng/SSPC-Net) 57 | * [One thing one click: A self-training approach for weakly supervised 3d semantic segmentation](https://ieeexplore.ieee.org/abstract/document/9578763) \[2021 CVPR\] :octocat:[code](https://github.com/liuzhengzhe/One-Thing-One-Click) 58 | * [Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation](https://ieeexplore.ieee.org/abstract/document/9709941) \[2021 ICCV\] :octocat:[code](https://github.com/GuoPingPan/RPVNet) 59 | * [Superpoint-guided semisupervised semantic segmentation of 3d point clouds](https://ieeexplore.ieee.org/document/9811904) \[2022 ICRA\] 60 | * [Pointdc: Unsupervised semantic segmentation of 3d point clouds via cross-modal distillation and super-voxel clustering](https://ieeexplore.ieee.org/document/10377176) \[2023 ICCV\] :octocat:[code](https://github.com/SCUT-BIP-Lab/PointDC) 61 | * [Point-to-voxel knowledge distillation for lidar semantic segmentation](https://ieeexplore.ieee.org/document/9879674) \[2022 CVPR\] :octocat:[code](https://github.com/cardwing/Codes-for-PVKD) 62 | * [Pointdc: Unsupervised semantic segmentation of 3d point clouds via cross-modal distillation and super-voxel clustering](https://ieeexplore.ieee.org/document/10377176) \[2023 ICCV\] :octocat:[code](https://github.com/SCUT-BIP-Lab/PointDC) 63 | * [Nested architecture search for point cloud semantic segmentation](https://ieeexplore.ieee.org/document/9919408) \[2023 TIP\] :octocat:[code](https://github.com/fanyang587/NestedNet) 64 | * [Multi-to-single knowledge distillation for point cloud semantic segmentation](https://ieeexplore.ieee.org/document/10160496) \[2023 ICRA\] :octocat:[code](https://github.com/skyshoumeng/M2SKD) 65 | * [Pointdistiller: Structured knowledge distillation towards efficient and compact 3d detection](https://ieeexplore.ieee.org/abstract/document/10205029) \[2023 CVPR\] :octocat:[code](https://github.com/RunpeiDong/PointDistiller) 66 | * [Knowledge distillation from 3d to bird’s-eye-view for lidar semantic segmentation](https://ieeexplore.ieee.org/document/10220057) \[2023 ICME\] :octocat:[code](https://github.com/fengjiang5/Knowledge-Distillation-from-Cylinder3D-to-PolarNet) 67 | * [Efficient 3D Semantic Segmentation with Superpoint Transformer](https://arxiv.org/abs/2306.08045) \[2023 ICCV\] :octocat:[code](https://github.com/drprojects/superpoint_transformer) 68 | * [Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering](https://arxiv.org/abs/2401.06704) \[2024 3DV\] :octocat:[code](https://github.com/drprojects/superpoint_transformer) 69 | 70 | #### 1.1.3 Refined Segmentation Goal 71 | ##### (1) Generalization of Segmentation 72 | * [Complete & label: A domain adaptation approach to semantic segmentation of lidar point clouds](https://ieeexplore.ieee.org/document/9578920) \[2021 CVPR\] 73 | * [Few-shot 3d point cloud semantic segmentation](https://ieeexplore.ieee.org/document/9577428) \[2021 CVPR\] :octocat:[code](https://github.com/Na-Z/attMPTI) 74 | * [Perceptionaware multi-sensor fusion for 3d lidar semantic segmentation](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhuang_Perception-Aware_Multi-Sensor_Fusion_for_3D_LiDAR_Semantic_Segmentation_ICCV_2021_paper.pdf) \[2021 ICCV\] :octocat:[code](https://github.com/ICEORY/PMF) 75 | * [Sparse-to-dense feature matching: Intra and inter domain cross-modal learning in domain adaptation for 3d semantic segmentation](https://ieeexplore.ieee.org/document/9710520) \[2021 ICCV\] :octocat:[code](https://github.com/leolyj/DsCML) 76 | * [Seggroup: Seglevel supervision for 3d instance and semantic segmentation](https://ieeexplore.ieee.org/document/9833393) \[2022 TIP\] :octocat:[code](https://github.com/antao97/SegGroup) 77 | * [Lif-seg: Lidar and camera image fusion for 3d lidar semantic segmentation](https://ieeexplore.ieee.org/document/10128757) \[2023 TMM\] 78 | * [Cross-modal learning for domain adaptation in 3d semantic segmentation](https://ieeexplore.ieee.org/document/9737217) \[2023 TPAMI\] :octocat:[code](https://github.com/valeoai/xmuda_journal) 79 | * [Pointglr: Unsupervised structural representation learning of 3d point clouds](https://ieeexplore.ieee.org/document/9736689) \[2023 TPAMI\] :octocat:[code](https://github.com/raoyongming/PointGLR) 80 | * [Prototype adaption and projection for few- and zero-shot 3d point cloud semantic segmentation](https://ieeexplore.ieee.org/document/10138737/) \[2023 TIP\] :octocat:[code](https://github.com/heshuting555/PAP-FZS3D) 81 | * [A multi-phase camera-lidar fusion network for 3d semantic segmentation with weak supervision](https://ieeexplore.ieee.org/abstract/document/10035004) \[2023 TCSVT\] 82 | * [Geometry and uncertainty-aware 3d point cloud class-incremental semantic segmentation](https://ieeexplore.ieee.org/document/10204829) \[2023 CVPR\] :octocat:[code](https://github.com/leolyj/3DPC-CISS) 83 | * [Novel class discovery for 3d point cloud semantic segmentation](https://ieeexplore.ieee.org/document/10203892) \[2023 CVPR\] :octocat:[code](https://github.com/LuigiRiz/NOPS) 84 | * [Lasermix for semi-supervised lidar semantic segmentation](https://ieeexplore.ieee.org/document/10205234) \[2023 CVPR\] :octocat:[code](https://github.com/ldkong1205/LaserMix) 85 | * [Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation](https://openaccess.thecvf.com/content/CVPR2023/html/Li_Less_Is_More_Reducing_Task_and_Model_Complexity_for_3D_CVPR_2023_paper.html) \[2023 CVPR\] :octocat:[code](https://github.com/l1997i/lim3d) 86 | * [PLA: Language-Driven Open-Vocabulary 3D Scene Understanding](https://openaccess.thecvf.com/content/CVPR2023/html/Ding_PLA_Language-Driven_Open-Vocabulary_3D_Scene_Understanding_CVPR_2023_paper.html) \[2023 CVPR\] :octocat:[code](https://dingry.github.io/projects/PLA) 87 | * [Growsp: Unsupervised semantic segmentation of 3d point clouds](https://ieeexplore.ieee.org/abstract/document/10203698) \[2023 CVPR\] :octocat:[code](https://github.com/vLAR-group/GrowSP) 88 | * [MSeg3D: Multi-Modal 3D Semantic Segmentation for Autonomous Driving](https://ieeexplore.ieee.org/document/10203290) \[2023 CVPR\] :octocat:[code](https://github.com/jialeli1/lidarseg3d) 89 | * [Label-guided knowledge distillation for continual semantic segmentation on 2d images and 3d point clouds](https://ieeexplore.ieee.org/document/10377766) \[2023 ICCV\] :octocat:[code](https://github.com/Ze-Yang/LGKD) 90 | * [Zero-shot point cloud segmentation by semantic-visual aware synthesis](https://ieeexplore.ieee.org/document/10377426) \[2023 ICCV\] :octocat:[code](https://github.com/leolyj/3DPC-GZSL) 91 | * [Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation](https://openaccess.thecvf.com/content/ICCV2023/html/Saltori_Walking_Your_LiDOG_A_Journey_Through_Multiple_Domains_for_LiDAR_ICCV_2023_paper.html) \[2023 ICCV\] :octocat:[code](https://github.com/saltoricristiano/lidog) 92 | * [Hierarchical pointbased active learning for semi-supervised point cloud semantic segmentation](https://openaccess.thecvf.com/content/ICCV2023/html/Xu_Hierarchical_Point-based_Active_Learning_for_Semi-supervised_Point_Cloud_Semantic_Segmentation_ICCV_2023_paper.html) \[2023 ICCV\] :octocat:[code](https://github.com/SmiletoE/HPAL) 93 | * [Cpcm: Contextual point cloud modeling for weaklysupervised point cloud semantic segmentation](https://openaccess.thecvf.com/content/ICCV2023/html/Liu_CPCM_Contextual_Point_Cloud_Modeling_for_Weakly-supervised_Point_Cloud_Semantic_ICCV_2023_paper.html) \[2023 ICCV\] :octocat:[code](https://github.com/lizhaoliu-Lec/CPCM) 94 | * [Towards open vocabulary learning: A survey](https://ieeexplore.ieee.org/document/10420487) \[2024 TPAMI\] 95 | * [RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding](https://openaccess.thecvf.com/content/CVPR2024/html/Yang_RegionPLC_Regional_Point-Language_Contrastive_Learning_for_Open-World_3D_Scene_Understanding_CVPR_2024_paper.html) \[2024 CVPR\] 96 | * [Dense supervision propagation for weakly supervised semantic segmentation on 3d point clouds](https://ieeexplore.ieee.org/document/10328634) \[2024 TCSVT\] 97 | 98 | ##### (2) Real-time Segmentation 99 | * [Lightningnet: Fast and accurate semantic segmentation for autonomous driving based on 3d lidar point cloud](https://ieeexplore.ieee.org/document/9102769) \[2020 ICME\] 100 | * [Lite-hdseg: Lidar semantic segmentation using lite harmonic dense convolutions](https://ieeexplore.ieee.org/document/9561171) \[2021 ICRA\] 101 | * [Stage-aware feature alignment network for real-time semantic segmentation of street scenes](https://ieeexplore.ieee.org/document/9583294) \[2022 TCSVT\] 102 | * [3d semantic segmentation of aerial photogrammetry models based on orthographic projection](https://ieeexplore.ieee.org/document/10119167) \[2023 TCSVT\] 103 | * [Knowledge distillation from 3d to bird’s-eye-view for lidar semantic segmentation](https://ieeexplore.ieee.org/document/10220057) \[2023 ICME\] :octocat:[code](https://github.com/fengjiang5/Knowledge-Distillation-from-Cylinder3D-to-PolarNet) 104 | * [Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving](https://ieeexplore.ieee.org/document/10204428) \[2023 CVPR\] :octocat:[code](https://github.com/valeoai/rangevit) 105 | * [Bird's-Eye-View Semantic Segmentation With Two-Stream Compact Depth Transformation and Feature Rectification](https://ieeexplore.ieee.org/abstract/document/10124335) \[2023 TIV\] 106 | * [Residual graph convolutional network for bird’seye-view semantic segmentation](https://ieeexplore.ieee.org/document/10483624) \[2024 WACV\] 107 | 108 | ##### (3) Point-based Semantic Segmentation 109 | * [Pointnet++: Deep hierarchical feature learning on point sets in a metric space](https://dl.acm.org/doi/abs/10.5555/3295222.3295263) \[2017 NIPS\] :octocat:[code](https://github.com/charlesq34/pointnet2) 110 | * [Point transformer](https://ieeexplore.ieee.org/document/9710703) \[2021 ICCV\] 111 | * [Backward attentive fusing network with local aggregation classifier for 3d point cloud semantic segmentation](https://ieeexplore.ieee.org/abstract/document/9410334) \[2021 TIP\] :octocat:[code](https://github.com/Xiangxu-0103/BAF-LAC) 112 | * [Cga-net: Category guided aggregation for point cloud semantic segmentation](https://ieeexplore.ieee.org/document/9577467) \[2021 CVPR\] :octocat:[code](https://github.com/MCG-NJU/CGA-Net) 113 | * [Point transformer v2: Grouped vector attention and partition-based pooling](https://papers.nips.cc/paper_files/paper/2022/hash/d78ece6613953f46501b958b7bb4582f-Abstract-Conference.html) \[2022 NIPS\] :octocat:[code](https://github.com/Gofinge/PointTransformerV2) 114 | * [Dcnet: Large-scale point cloud semantic segmentation with discriminative and efficient feature aggregation](https://ieeexplore.ieee.org/document/10025770) \[2023 TCSVT\] :octocat:[code](https://github.com/fukunyin/DCNet) 115 | * [Point transformer v3: Simpler, faster, stronger](https://arxiv.org/abs/2312.10035) \[2024 CVPR\] :octocat:[code](https://github.com/Pointcept/PointTransformerV3) 116 | 117 | ##### (4) Unit-sets-based on Semantic Segmentation 118 | * [Instance segmentation in 3d scenes using semantic superpoint tree networks](https://ieeexplore.ieee.org/document/9709996) \[2021 ICCV\] :octocat:[code](https://github.com/Gorilla-Lab-SCUT/SSTNet) 119 | * [Sspc-net: Semi-supervised semantic 3d point cloud segmentation network](https://ojs.aaai.org/index.php/AAAI/article/view/16200) \[2021 AAAI\] :octocat:[code](https://github.com/MMCheng/SSPC-Net) 120 | * [One thing one click: A self-training approach for weakly supervised 3d semantic segmentation](https://ieeexplore.ieee.org/abstract/document/9578763) \[2021 CVPR\] :octocat:[code](https://github.com/liuzhengzhe/One-Thing-One-Click) 121 | * [Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation](https://ieeexplore.ieee.org/abstract/document/9709941) \[2021 ICCV\] :octocat:[code](https://github.com/GuoPingPan/RPVNet) 122 | * [Superpoint-guided semisupervised semantic segmentation of 3d point clouds](https://ieeexplore.ieee.org/document/9811904) \[2022 ICRA\] 123 | * [Pointdc: Unsupervised semantic segmentation of 3d point clouds via cross-modal distillation and super-voxel clustering](https://ieeexplore.ieee.org/document/10377176) \[2023 ICCV\] :octocat:[code](https://github.com/SCUT-BIP-Lab/PointDC) 124 | * [Point-to-voxel knowledge distillation for lidar semantic segmentation](https://ieeexplore.ieee.org/document/9879674) \[2022 CVPR\] :octocat:[code](https://github.com/cardwing/Codes-for-PVKD) 125 | * [Pointdc: Unsupervised semantic segmentation of 3d point clouds via cross-modal distillation and super-voxel clustering](https://ieeexplore.ieee.org/document/10377176) \[2023 ICCV\] :octocat:[code](https://github.com/SCUT-BIP-Lab/PointDC) 126 | * [Nested architecture search for point cloud semantic segmentation](https://ieeexplore.ieee.org/document/9919408) \[2023 TIP\] :octocat:[code](https://github.com/fanyang587/NestedNet) 127 | * [Multi-to-single knowledge distillation for point cloud semantic segmentation](https://ieeexplore.ieee.org/document/10160496) \[2023 ICRA\] :octocat:[code](https://github.com/skyshoumeng/M2SKD) 128 | * [Pointdistiller: Structured knowledge distillation towards efficient and compact 3d detection](https://ieeexplore.ieee.org/abstract/document/10205029) \[2023 CVPR\] :octocat:[code](https://github.com/RunpeiDong/PointDistiller) 129 | * [Knowledge distillation from 3d to bird’s-eye-view for lidar semantic segmentation](https://ieeexplore.ieee.org/document/10220057) \[2023 ICME\] :octocat:[code](https://github.com/fengjiang5/Knowledge-Distillation-from-Cylinder3D-to-PolarNet) 130 | 131 | ### 1.2 Point Cloud Compression :small_blue_diamond: 132 | * [Lossless point cloud geometry and attribute compression using a learned conditional probability model](https://ieeexplore.ieee.org/document/10024999) \[2023 TCSVT\] :octocat:[code](https://github.com/Weafre/CNeT) 133 | * [Hm-pcgc: A human-machine balanced point cloud geometry compression scheme](https://ieeexplore.ieee.org/document/10222524) \[2023 ICIP\] 134 | * [Pchm-net: A new point cloud compression framework for both human vision and machine vision](https://ieeexplore.ieee.org/document/10219641) \[2023 ICME\] 135 | * [A task-driven scene-aware lidar point cloud coding framework for autonomous vehicles](https://ieeexplore.ieee.org/document/9944923) \[2023 TII\] 136 | * [Inter-frame compression for dynamic point cloud geometry coding](https://ieeexplore.ieee.org/document/10380494) \[2024 TIP\] 137 | * [Occupancy-assisted attribute artifact reduction for video-based point cloud compression](https://ieeexplore.ieee.org/document/10416804) \[2024 TB\] 138 | * [A unified framework for jointly compressing visual and semantic data](https://dl.acm.org/doi/10.1145/3654800) \[2024 ACM\] 139 | ### 1.3 Point Cloud Registration :small_blue_diamond: 140 | * [Probabilistic data association for semantic slam](https://ieeexplore.ieee.org/document/7989203) \[2017 ICRA\] 141 | * [Integrating deep semantic segmentation into 3-d point cloud registration](https://ieeexplore.ieee.org/document/8387438) \[2018 RAL\] 142 | * [Fast point cloud registration using semantic segmentation](https://ieeexplore.ieee.org/document/8945870) \[2019 DICTA\] 143 | * [Robust point set registration based on semantic information](https://ieeexplore.ieee.org/document/9282862) \[2020 SMC\] 144 | * [Point set registration with semantic region association using cascaded expectation maximization](https://ieeexplore.ieee.org/document/9561140) \[2021 ICRA\] 145 | * [A new framework for registration of semantic point clouds from stereo and rgb-d cameras](https://ieeexplore.ieee.org/document/9561929) \[2021 ICRA\] :octocat:[code](https://github.com/UMich-CURLY/unified_cvo) 146 | * [Partial-to-partial point cloud registration based on multi-level semantic-structural cognition](https://ieeexplore.ieee.org/document/9860002) \[2022 ICME\] 147 | * [Segregator: Global point cloud registration with semantic and geometric cues](https://ieeexplore.ieee.org/document/10160798) \[2023 ICRA\] :octocat:[code](https://github.com/Pamphlett/Segregator) 148 | * [Pyramid semantic graph-based global point cloud registration with low overlap](https://ieeexplore.ieee.org/document/10341394) \[2023 IROS\] :octocat:[code](https://github.com/HKUST-Aerial-Robotics/Pagor) 149 | * [Deepsir: Deep semantic iterative registration for lidar point clouds](https://dl.acm.org/doi/10.1016/j.patcog.2023.109306) \[2023 Pattern Recognit\] :octocat:[code](https://github.com/LeoQLi/DeepSIR) 150 | ### 1.4 Point Cloud Reconstruction :small_blue_diamond: 151 | * [Rfd-net: Point scene understanding by semantic instance reconstruction](https://ieeexplore.ieee.org/document/9578585) \[2021 CVPR\] 152 | * [Buildingfusion: Semantic-aware structural building-scale 3d reconstruction](https://ieeexplore.ieee.org/document/9286413) \[2022 TPAMI\] 153 | * [Real-time globally consistent 3d reconstruction with semantic priors](https://ieeexplore.ieee.org/document/9662197) \[2023 TVCG\] 154 | * [Sg-nerf: Semantic-guided point-based neural radiance fields](https://ieeexplore.ieee.org/document/10219715) \[2023 ICME\] 155 | * [Navinerf: Nerf-based 3d representation disentanglement by latent semantic navigation](https://ieeexplore.ieee.org/document/10377982) \[2023 ICCV\] 156 | * [NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields](https://arxiv.org/pdf/2304.14811) \[2024 AAAI\] :octocat:[code](https://github.com/fudan-zvg/NeRF-LiDAR) 157 | 158 | 159 | ## 2. New Point Cloud Tasks with Semantic :diamond_shape_with_a_dot_inside: 160 | 161 | ### 2.1 3D Scene Understanding :small_blue_diamond: 162 | #### 2.1.1 Scene Graph Prediction 163 | * [Learning 3d semantic scene graphs from 3d indoor reconstructions](https://ieeexplore.ieee.org/document/9156565) \[2020 CVPR\] 164 | * [Scenegraphfusion: Incremental 3d scene graph prediction from rgb-d sequences](https://ieeexplore.ieee.org/document/9578559) \[2021 CVPR\] 165 | * [Exploiting edge-oriented reasoning for 3d point-based scene graph analysis](https://ieeexplore.ieee.org/document/9578123) \[2021 CVPR\] :octocat:[code](https://SGGpoint.github.io) 166 | * [Knowledge-inspired 3d scene graph prediction in point cloud](https://proceedings.neurips.cc/paper/2021/file/9a555403384fc12f931656dea910e334-Paper.pdf) \[2021 NIPS\] 167 | * [Graph-to-3d: End-to-end generation and manipulation of 3d scenes using scene graphs](https://ieeexplore.ieee.org/document/9710451) \[2021 ICCV\] :octocat:[code]( https://he-dhamo.github.io/Graphto3D/) 168 | * [Learning 3d semantic scene graphs with instance embeddings](https://link.springer.com/article/10.1007/s11263-021-01546-9) \[2022 IJCV\] 169 | * [Vl-sat: Visual-linguistic semantics assisted training for 3d semantic scene graph prediction in point cloud](https://ieeexplore.ieee.org/document/10205194) \[2023 CVPR\] :octocat:[code](https://github.com/wz7in/CVPR2023-VLSAT) 170 | * [Sgrec3d: Self-supervised 3d scene graph learning via object-level scene reconstruction](https://ieeexplore.ieee.org/document/10484453) \[2024 WACV\] 171 | * [Commonscenes: Generating commonsense 3d indoor scenes with scene graphs](https://proceedings.neurips.cc/paper_files/paper/2023/file/5fba70900a84a8fb755c48ba99420c95-Paper-Conference.pdf) \[2024 NIPS\] 172 | 173 | #### 2.1.2 3D vision with language 174 | ##### (1) 3D Dense Captioning 175 | * [Scan2cap: Context-aware dense captioning in rgb-d scans](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Scan2Cap_Context-Aware_Dense_Captioning_in_RGB-D_Scans_CVPR_2021_paper.pdf) \[2021 CVPR\] :octocat:[code](https://github.com/daveredrum/Scan2Cap) 176 | * [Free-form description guided 3d visual graph network for object grounding in point cloud](https://ieeexplore.ieee.org/document/9710755) \[2021 ICCV\] :octocat:[code](https://github.com/PNXD/FFL-3DOG) 177 | * [Spatiality-guided transformer for 3d dense captioning on point clouds](https://arxiv.org/abs/2204.10688) \[2022 arxiv\] :octocat:[code](https://spacap3d.github.io/) 178 | * [X-trans2cap: Cross-modal knowledge transfer using transformer for 3d dense captioning](https://ieeexplore.ieee.org/document/9879338) \[2022 CVPR\] 179 | * [3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds](https://ieeexplore.ieee.org/document/9879358) \[2022 CVPR\] 180 | * [A comprehensive survey of 3d dense captioning: Localizing and describing objects in 3d scenes](https://ieeexplore.ieee.org/document/10187165) \[2024 TCSVT\] 181 | ##### (2) 3D Point Cloud Localization 182 | * [Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition](https://ieeexplore.ieee.org/document/8578568) \[2018 CVPR\] :octocat:[code](https://github.com/mikacuy/pointnetvlad) 183 | * [Soe-net: A self-attention and orientation encoding network for point cloud based place recognition](https://ieeexplore.ieee.org/document/9577773) \[2021 CVPR\] :octocat:[code](https://github.com/Yan-Xia/SOE-Net) 184 | * [Text2Pos: Text-to-Point-Cloud Cross-Modal Localization](https://ieeexplore.ieee.org/document/9880174) \[2022 CVPR\] :octocat:[code](https://github.com/mako443/Text2Pos-CVPR2022) 185 | * [Text to point cloud localization with relation-enhanced transformer](https://ojs.aaai.org/index.php/AAAI/article/view/25347) \[2023 AAAI\] :octocat:[code](https://github.com/daoyuan98/text2pos-ret) 186 | * [Text2Loc: 3D Point Cloud Localization from Natural Language](https://openaccess.thecvf.com/content/CVPR2024/papers/Xia_Text2Loc_3D_Point_Cloud_Localization_from_Natural_Language_CVPR_2024_paper.pdf) \[2024 CVPR\] :octocat:[code](https://github.com/Yan-Xia/Text2Loc) 187 | 188 | ### 2.2 Point Cloud Semantic Scene Completion :small_blue_diamond: 189 | * [See and think: Disentangling semantic scene completion](https://dl.acm.org/doi/10.5555/3326943.3326968) \[2018 NIPS\] :octocat:[code](https://github.com/ShiceLiu/SATNet) 190 | * [3D Semantic Scene Completion from a Single Depth Image Using Adversarial Training](https://ieeexplore.ieee.org/abstract/document/8803174) \[2019 ICIP\] :octocat:[code](https://github.com/shurans/sscnet) 191 | * [Cascaded context pyramid for full-resolution 3d semantic scene completion](https://ieeexplore.ieee.org/document/9008381) \[2019 ICCV\] 192 | * [3d sketch-aware semantic scene completion via semi-supervised structure prior](https://ieeexplore.ieee.org/document/9156418) \[2020 CVPR\] :octocat:[code](https://github.com/CV-IP/3D-SketchAware-SSC) 193 | * [Attention-based multimodal fusion network for semantic scene completion](https://ojs.aaai.org/index.php/AAAI/article/view/6803) \[2020 AAAI\] 194 | * [Semantic scene completion using local deep implicit functions on lidar data](https://ieeexplore.ieee.org/abstract/document/9477025) \[2022 TPAMI\] 195 | * [Voxformer: Sparse voxel transformer for camerabased 3d semantic scene completion](https://ieeexplore.ieee.org/document/10203337) \[2023 CVPR\] :octocat:[code](https://github.com/NV1abs/VoxFormer) 196 | * [Scpnet: Semantic scene completion on point cloud](https://ieeexplore.ieee.org/document/10203998) \[2023 CVPR\] :octocat:[code](https://github.com/SCPNet/Codes-for-SCPNet) 197 | * [Occdepth: A depth-aware method for 3d semantic scene completion](https://arxiv.org/abs/2302.13540) \[2023 arxiv\] :octocat:[code](https://github.com/megvii-research/OccDepth) 198 | * [Ddit: Semantic scene completion via deformable deep implicit templates](https://ieeexplore.ieee.org/document/10376787) \[2023 ICCV\] 199 | * [Cvsformer: Cross-view synthesis transformer for semantic scene completion](https://ieeexplore.ieee.org/abstract/document/10378387) \[2023 ICCV\] :octocat:[code](https://github.com/donghaotian123/CVSformer.) 200 | * [Ndc-scene: Boost monocular 3d semantic scene completion in normalized device coordinates space](https://ieeexplore.ieee.org/document/10376597) \[2023 ICCV\] :octocat:[code](https://github.com/Jiawei-Yao0812/NDCScene) 201 | * [Esc-net: Alleviating triple sparsity on 3d lidar point clouds for extreme sparse scene completion](https://ieeexplore.ieee.org/document/10409585) \[2024 TMM\] 202 | * [Symphonize 3D Semantic Scene Completion with Contextual Instance Queries](https://arxiv.org/pdf/2306.15670) \[2024 CVPR\] :octocat:[code](https://github.com/hustvl/Symphonies) 203 | * [Unleashing Network Potentials for Semantic Scene Completion](https://arxiv.org/pdf/2403.07560v1) \[2024 CVPR\] :octocat:[code](https://github.com/fereenwong/AMMNet) 204 | * [SemCity: Semantic Scene Generation with Triplane Diffusion](https://arxiv.org/pdf/2403.07773v1) \[2024 CVPR\] :octocat:[code](https://github.com/zoomin-lee/SemCity) 205 | * [VFG-SSC: Semi-supervised 3D Semantic Scene Completion with 2D Vision Foundation Model Guidance](https://arxiv.org/abs/2408.11559) \[2025 AAAI\] 206 | * [MixSSC: Forward-Backward Mixture for Vision-based 3D Semantic Scene Completion]([https://arxiv.org/abs/2408.11559](https://ieeexplore.ieee.org/abstract/document/10833696)) \[2025 TCSVT\] :octocat:[code](https://github.com/willemeng/MixSSC) 207 | 208 | ### 2.3 Point Cloud Understanding :small_blue_diamond: 209 | #### 2.3.1 Integration Tasks in Point Clouds 210 | * [Jsis3d: Joint semantic-instance segmentation of 3d point clouds with multi-task pointwise networks and multi-value conditional random fields](https://ieeexplore.ieee.org/document/9412532) \[2020 ICPR\] :octocat:[code](https://github.com/pqhieu/jsis3d) 211 | * [Associatively segmenting instances and semantics in point clouds](https://ieeexplore.ieee.org/document/8953321) \[2019 CVPR\] :octocat:[code](https://github.com/WXinlong/ASIS) 212 | * [Semantic labeling and instance segmentation of 3d point clouds using patch context analysis and multiscale processing](https://ieeexplore.ieee.org/document/8590720) \[2020 TVCG\] 213 | * [Jsnet++: Dynamic filters and pointwise correlation for 3d point cloud instance and semantic segmentation](https://ieeexplore.ieee.org/document/9932589) \[2023 TCSVT\] :octocat:[code](https://github.com/dlinzhao/JSNetPP) 214 | * [Explore in-context learning for 3d point cloud understanding](https://papers.nips.cc/paper_files/paper/2023/file/8407d254b5baacf69ee977aa34f0e521-Paper-Conference.pdf) \[2023 NIPS\] :octocat:[code](https://github.com/fanglaosi/Point-In-Context) 215 | * [Multi-Space Alignments Towards Universal LiDAR Segmentation](https://arxiv.org/pdf/2405.01538v1) \[2024 CVPR\] :octocat:[code](https://github.com/youquanl/M3Net) 216 | * [X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition](https://arxiv.org/pdf/2404.15010) \[2024 CVPR\] :octocat:[code](https://github.com/sunshuofeng/X-3D) 217 | * [Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding](https://arxiv.org/pdf/2312.02244) \[2024 CVPR\] :octocat:[code](https://luigiriz.github.io/geoze-website/) 218 | #### 2.3.2 Multi-modality 219 | * [Pointclip: Point cloud understanding by clip](https://ieeexplore.ieee.org/document/9878980) \[2022 CVPR\] :octocat:[code](https://github.com/ZrrSkywalker/PointCLIP) 220 | * [Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding](https://ieeexplore.ieee.org/document/9878878) \[2022 CVPR\] :octocat:[code](https://github.com/MohamedAfham/CrossPoint) 221 | * [Leaf: Learning frames for 4d point cloud sequence understanding](https://ieeexplore.ieee.org/document/10377208) \[2023 ICCV\] 222 | * [Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following](https://arxiv.org/pdf/2309.00615) \[arxiv 2023\] :octocat:[code](https://github.com/ZiyuGuo99/Point-Bind_Point-LLM) 223 | * [Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion](https://proceedings.neurips.cc/paper_files/paper/2023/hash/a8f7f12b29d9b8c227785f6b529f63b7-Abstract-Conference.html) \[2023 NIPS\] :octocat:[code](https://github.com/tusen-ai/EchoFusion) 224 | * [MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding](https://arxiv.org/pdf/2402.10002) \[2024 AAAI\] :octocat:[code](https://github.com/HaydenYu/MM-Point) 225 | #### 2.3.3 Network Architecture Optimization 226 | * [ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding](https://dl.acm.org/doi/10.5555/3666122.3667159) \[2023 NIPS\] :octocat:[code](https://github.com/LHDuan/ConDaFormer) 227 | * [Self-Positioning Point-Based Transformer for Point Cloud Understanding](https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.pdf) \[2023 CVPR\] :octocat:[code](https://github.com/mlvlab/SPoTr) 228 | * [Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy](https://arxiv.org/abs/2403.06467) \[2024 arxiv\] :octocat:[code](https://github.com/IRMVLab/Point-Mamba) 229 | * [Point Cloud Mamba: Point Cloud Learning via State Space Model](https://arxiv.org/abs/2403.00762) \[2024 arxiv\] :octocat:[code](https://github.com/SkyworkAI/PointCloudMamba) 230 | --------------------------------------------------------------------------------