/Mesh_Segmentation

some materials about mesh processing, including papers, videos, codes, and so on. Updating every day!

Mesh Processing

I hope the branch can help anyone who wants to do research about mesh processing.

Contact me: qiujie_dong(AT)mail.sdu.edu.cn, Qiujie.Jay.Dong(AT)gmail.com.

Thanks for your valuable contribution to the research.:smiley:

- Symbols

Statistics: ⭐ code is available & stars >= 100  |  🔥 citation >= 50

- Topics


- Papers for Mesh Transformer

Related work

  • MCTformer: Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Dan Xu. "Multi-class Token Transformer for Weakly Supervised Semantic Segmentation", CVPR(2022). [paper] [code]

  • MHFormer: Wenhao Li, Hong Liu, Hao Tang, Pichao Wang, Luc Van Gool. "MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation", CVPR(2022). [paper] [code]

  • MetaFormer: Qishuai Diao, Yi Jiang, Bin Wen, Jia Sun, Zehuan Yuan. "MetaFormer: A Unified Meta Framework for Fine-Grained Recognition", arXiv(2022). [paper] [code]

  • ACT: Jinsu Yoo, Taehoon Kim, Sihaeng Lee, Seung Hwan Kim, Honglak Lee, Tae Hyun Kim. "Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution", arXiv(2022). [paper]

  • ShapeFormer: Xingguang Yan, Liqiang Lin, Niloy J. Mitra, Dani Lischinski, Danny Cohen-Or, Hui Huang. "ShapeFormer: Transformer-based Shape Completion via Sparse Representation", arXiv(2022). [project] [paper] [code]

  • EdgeFormer: Haokui Zhang, Wenze Hu, Xiaoyu Wang. "EdgeFormer: Improving Light-weight ConvNets by Learning from Vision Transformers", arXiv(2022). [paper]

  • DoT: Ren Chuan-Xian, Zhai Yi-Ming, Luo You-Wei, Li Meng-Xue. "Towards Unsupervised Domain Adaptation via Domain-Transformer", arXiv(2022). [paper]

  • Swin Transformer: Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows", ICCV(2021). [paper] [code] ⭐🔥

  • DeiT: Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou. "Training data-efficient image transformers & distillation through attention", ICML(2021). [paper] [code] ⭐🔥

  • 3DMedPT: Jianhui Yu, Chaoyi Zhang, Heng Wang, Dingxin Zhang, Yang Song, Tiange Xiang, Dongnan Liu, Weidong Cai. "3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis", arXiv(2021). [paper] [code]

  • ViT: Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale", ICLR(2021) . [paper] [code] ⭐🔥

  • PCT: Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R. Martin, Shi-Min Hu. "PCT: Point Cloud Transformer", arXiv(2020). [paper] [code] ⭐🔥

- Papers for Mesh Feature Extraction

2022

  • Laplacian2Mesh: Qiujie Dong, Zixiong Wang, Junjie Gao, Shuangmin Chen, Zhenyu Shu, Shiqing Xin. "Laplacian2Mesh: Laplacian-Based Mesh Understanding", arXiv(2022). [paper] [code]

  • DiffusionNet: Nicholas Sharp, Souhaib Attaiki, Keenan Crane, Maks Ovsjanikov. "DiffusionNet: Discretization Agnostic Learning on Surfaces", TOG(2022). [paper] [code]

  • SubdivNet: Shi-Min Hu, Zheng-Ning Liu, Meng-Hao Guo, Jun-Xiong Cai, Jiahui Huang, Tai-Jiang Mu, Ralph R. Martin. "Subdivision-Based Mesh Convolution Networks", TOG(2022). [paper] [code]

  • Laplacian Mesh Transformer: Xiao-Juan Li, Jie Yang, Fang-Lue Zhan. "Laplacian Mesh Transformer: Dual Attention and Topology Aware Network for 3D Mesh Classification and Segmentation", ECCV(2022). [paper]

2021

  • HodgeNet: Dmitriy Smirnov, Justin Solomon. "HodgeNet: Learning Spectral Geometry on Triangle Meshes", SIGGRAPH( 2021). [paper] [code]

2020

  • PD-MeshNet: Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone. "Primal-Dual Mesh Convolutional Neural Networks", NeurIPS(2020) . [paper] [code]

  • CurvaNet: Wenchong He, Zhe Jiang, Chengming Zhang, Arpan Man Sainju. "CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis", KDD(2020). [paper]

  • MeshSegNet: Chunfeng Lian, Li Wang, Tai-Hsien Wu, Fan Wang, Pew-Thian Yap, Ching-Chang Ko, Dinggang Shen. "Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners", MICCAI( 2019) and TMI(2020) . [paper] [code]

  • MGCN: Yiqun Wang, Jing Ren, Dong-Ming Yan, Jianwei Guo, Xiaopeng Zhang, Peter Wonka. "MGCN: Descriptor Learning using Multiscale GCNs", SIGGRAPH(2020) . [project] [paper] [code]

  • MedMeshCNN: Lisa Schneider, Annika Niemann, Oliver Beuing, Bernhard Preim, Sylvia Saalfeld. "MedMeshCNN - Enabling MeshCNN for Medical Surface Models", arXiv(2020) . [paper] [code]

  • MeshWalker: Alon Lahav, Ayellet Tal. "MeshWalker: Deep Mesh Understanding by Random Walks", SIGGRAPH Asia(2020) . [paper] [code]

  • Amit Kohli, Vincent Sitzmann, Gordon Wetzstein. "Semantic Implicit Neural Scene RepresentationsWith Semi-Supervised Training", 3DV(2020). [project] [paper] [code]

  • Zhenyu Shu, Xiaoyong Shen, Shiqing Xin, Qingjun Chang, Jieqing Feng, Ladislav Kavan, Ligang Liu. "Scribble-Based 3D Shape Segmentation via Weakly-Supervised Learning", TVCG(2020). [paper]

2019

  • LaplacianNet: Yi-Ling Qiao, Lin Gao, Jie Yang, Paul L. Rosin, Yu-Kun Lai, Xilin Chen. "LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling", TVCG(2019). [paper]

  • VoxSegNet: Zongji Wang, Feng Lu. "VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes", TVCG( 2019). [paper] [code]

  • BAE-Net: Chen Zhiqin, Yin Kangxue, Fisher Matthew, Chaudhuri Siddhartha, Zhang Hao. "Bae-net: Branched autoencoder for shape co-segmentation", ICCV(2019) . [paper] [code]

  • MeshNet: Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao, Yue Gao. "MeshNet: Mesh Neural Network for 3D Shape Representation", AAAI(2019) . [paper] [code] ⭐

  • DGCNN: Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon. "Dynamic Graph CNN for Learning on Point Clouds", TOG(2019) . [project] [paper] [code] ⭐🔥

  • MeshCNN: Hanocka Rana, Hertz Amir, Fish Noa, Giryes Raja, Fleishman Shachar, Cohen-Or Daniel. "MeshCNN: A Network with an Edge", SIGGRAPH(2019) . [project] [paper] [code] [code from NVIDIA] ⭐🔥

  • Xiaojie Xu, Chang Liu, Youyi Zheng. "3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks", TVCG(2019). [paper]

  • Zhao Wang; Li Chen. "Mesh Segmentation for High Resolution Medical Data", CISP-BMEI(2019) . [paper]

Before 2019

  • George David, Xie Xianghua, Tam Gary KL. "3D mesh segmentation via multi-branch 1D convolutional neural networks", GM( 2018). [paper]

  • A Survey: Rui S. V. Rodrigues, Jos´e F. M. Morgado, Abel J. P. Gomes. "Part‐Based Mesh Segmentation: A Survey", COMPUTER GRAPHICS forum(2018). [paper]

  • Pengyu Wang, Yuan Gan, Panpan Shui, Fenggen Yu, Yan Zhang, Songle Chen, Zhengxing Sun. "3D Shape Segmentation via Shape Fully Convolutional Networks", CG(2018) . [paper] [code]

  • Pointgrid: Truc Le, Ye Duan. "Pointgrid: A deep network for 3d shape understanding", CVPR(2018) . [paper] [code_PyTorch] [code_TensorFlow] 🔥

  • PointCNN: Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, Baoquan Chen. "PointCNN: Convolution On X-Transformed Points", NIPS(2018) . [paper] [code] ⭐🔥

  • SyncSpecCNN: Li Yi, Hao Su, Xingwen Guo, Leonidas Guibas. "SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation", CVPR(2017). [paper] [code] 🔥

  • DCN: Haotian Xu, Ming Dong, Zichun Zhong. "Directionally convolutional networks for 3d shape segmentation", ICCV(2017) . [paper]

  • Shubham Tulsiani, Hao Su, Leonidas J. Guibas, Alexei A. Efros, Jitendra Malik. "Learning shape abstractions by assembling volumetric primitives", CVPR(2017) . [project] [paper] [code] ⭐🔥

  • MVRNN: Le Truc, Bui Giang, Duan Ye. "A multi-view recurrent neural network for 3D mesh segmentation", Computers & Graphics(2017) . [paper] [code]

  • A Survey: Medhat Rashad, Mohamed Khamiss, Mohamed MOUSA. "A Review on Mesh Segmentation Techniques", IJEIT(2017) . [paper]

  • ShapePFCN: Evangelos Kalogerakis, Melinos Averkiou, Subhransu Maji, Siddhartha Chaudhuri. "3D Shape Segmentation with Projective Convolutional Networks", CVPR(2017) . [project] [paper] [code] 🔥

  • Panagiotis Theologou, Ioannis Pratikakis, Theoharis Theoharis. "Unsupervised spectral mesh segmentation driven by heterogeneous graphs", TPAMI(2016). [paper]

  • Zhenyu Shu, Chengwu Qi, Shiqing Xin, Chao Hu, Li Wang, Yu Zhang, Ligang Liu. "Unsupervised 3D shape segmentation and co-segmentation via deep learning", CAGD(2016) . [paper] 🔥

  • Meha Hachani, Azza Ouled Zaid, Raoua Khwildi. "Segmentation of 3D articulated meshes using shape diameter function and curvature information", ICME(2016). [paper]

  • Kan Guo, Dongqing Z, Xiaowu Chen. "3D Mesh Labeling via Deep Convolutional Neural Networks", TOG(2015) . [paper] 🔥

  • MVCNN: Su Hang, Maji Subhransu, Kalogerakis Evangelos, Learned-Miller Erik. "Multi-view Convolutional Neural Networks for 3D Shape Recognition", ICCV(2015) . [project] [paper] [code] ⭐🔥

  • Zhige Xie, Kai Xu, Ligang Liu, Yueshan Xiong. "3D Shape Segmentation and Labeling via Extreme Learning Machine", CGF( 2014). [paper] 🔥

  • Jung Lee, Seokhun Kim, Sun-Jeong Kim. "Mesh segmentation based on curvatures using the GPU", MTA(2014). [paper]

  • Zizhao Wu, Yunhai Wang, Ruyang Shou, Baoquan Chen, Xinguo Liu. "Unsupervised co-segmentation of 3D shapes via affinity aggregation spectral clustering", CG(2013) . [paper]

  • Yunhai Wang, Minglun Gong, Tianhua Wang, Daniel Cohen-Or, Hao Zhang, Baoquan Chen. "Projective analysis for 3D shape segmentation", TOG(2013). [paper] 🔥

  • Jiajun Lv, Xinlei Chen, Jin Huang, Hujun Bao. "Semi-supervised Mesh Segmentation and Labeling", CGF(2012) . [paper]

  • Hu Ruizhen, Fan Lubin, Liu Ligang. "Co‐segmentation of 3d shapes via subspace clustering", CGF(2012) . [paper] 🔥

  • Oscar Kin-Chung Au, Youyi Zheng, Menglin Chen, Pengfei Xu, Chiew-Lan Tai. "Mesh Segmentation with Concavity-aware Fields", TVCG(2012). [paper] 🔥

  • Heat-Mapping: Yi Fang, Mengtian Sun, Minhyong Kim, Karthik Ramani. "Heat-mapping: A robust approach toward perceptually consistent mesh segmentation", CVPR(2011) . [paper] 🔥

  • Jun Wang, Zeyun Yu. "Surface feature based mesh segmentation", CG(2011) . [paper]

  • Evangelos Kalogerakis, Aaron Hertzmann, Karan Singh. "Learning 3D Mesh Segmentation and Labeling", SIGGRAPH(2010) . [paper] 🔥

  • Avinash Sharma, Radu Horaud, David Knossow, Etienne von Lavante. "Mesh Segmentation Using Laplacian Eigenvectors and Gaussian Mixtures", AAAI(2009). [paper]

  • Curvature Laplacian: Rong Liu, Hao Zhang. "Mesh Segmentation via Spectral Embedding and Contour Analysis", CGF( 2007). [paper] 🔥

  • Raif M. Rustamov. "Laplace-Beltrami Eigenfunctions for Deformation Invariant Shape Representation", SGP(2007) . [paper] 🔥

  • A Survey: M. Attene, S. Katz, M. Mortara, G. Patane, M. Spagnuolo, A. Tal. "Mesh Segmentation - A Comparative Study", SMI(2006). [paper] 🔥

  • Sagi Katz, George Leifman, Ayellet Tal. "Mesh segmentation using feature point and core extraction", TVC(2005) . [paper] 🔥

  • Rong Liu, Hao Zhang. "Segmentation of 3D meshes through spectral clustering", PG(2004) . [paper] 🔥

Related work

  • Wave-MLP: Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu, Yanxi Li, Chao Xu, Yunhe Wang. "An Image Patch is a Wave: Quantum Inspired Vision MLP", CVPR(2022). [paper] [code] ⭐

  • BACON: David B. Lindell, Dave Van Veen, Jeong Joon Park, Gordon Wetzstein. "BACON: Band-limited Coordinate Networks for Multiscale Scene Representation", CVPR(2022). [project] [paper] [code]

  • PSSNet: Weixiao Gao, Liangliang Nan, Bas Boom, Hugo Ledoux. "PSSNet: Planarity-sensible Semantic Segmentation of Large-scale Urban Meshes", arXiv(2022). [paper]

  • Swin-Unet: Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, Manning Wang. "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation", arXiv(2021). [paper] [code] ⭐

  • TransUNet: Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L. Yuille, Yuyin Zhou. "TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation", arXiv(2021). [paper] [code] ⭐🔥

  • VMNet: Zeyu Hu, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai. "VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation", ICCV(2021). [paper] [code]

  • Deep3DMM: Zhixiang Chen, Tae-Kyun Kim. "Learning Feature Aggregation for Deep 3D Morphable Models", arXiv(2021). [paper] [code]

  • Luke Melas-Kyriazi. "Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet", arXiv(2021) . [paper] [code]

  • Geometric Deep Learning: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković. "Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges", arXiv(2021). [paper]

  • UNETR: Ali Hatamizadeh, Dong Yang, Holger Roth, Daguang Xu. "UNETR: Transformers for 3D Medical Image Segmentation", arXiv(2021). [paper]

  • A Survey: Yong He, Hongshan Yu, Xiaoyan Liu, Zhengeng Yang, Wei Sun, Yaonan Wang, Qiang Fu, Yanmei Zou, Ajmal Main. "Deep Learning based 3D Segmentation: A Survey", arXiv(2021). [paper]

  • U-Net Transformer: Olivier Petit, Nicolas Thome, Clément Rambour, Luc Soler. "U-Net Transformer: Self and Cross Attention for Medical Image Segmentation", arXiv(2021). [paper]

  • Maximilian Durner, Wout Boerdijk, Martin Sundermeyer, Werner Friedl, Zoltan-Csaba Marton, Rudolph Triebel. "Unknown Object Segmentation from Stereo Images", arXiv(2021). [paper]

  • Benjamin Caine, Rebecca Roelofs, Vijay Vasudevan, Jiquan Ngiam, Yuning Chai, Zhifeng Chen, Jonathon Shlens. " Pseudo-labeling for Scalable 3D Object Detection", arXiv(2021). [paper]

  • SPICE: Chuang Niu, Ge Wang. "SPICE: Semantic Pseudo-labeling for Image Clustering", arXiv(2021) . [paper] [code]

  • Megha Kalia, Tajwar Abrar Aleef, Nassir Navab, Septimiu E. Salcudean. "Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data", arXiv(2021). [paper]

  • MFNs: Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J.Zico Kolter. "Multiplicative Filter Networks", ICLR(2021). [paper] [code]

  • IDF: Wang Yifan, Lukas Rahmann, Olga Sorkine-Hornung. "Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields", ICLR(2022). [project] [paper] [code]

  • Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, Ren Ng. "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains", NeurIPS(2020). [project] [paper] [code] ⭐🔥

  • Nicholas Sharp, Keenan Crane. "A Laplacian for Nonmanifold Triangle Meshes", CGF(2020). [project] [paper] [code]

  • STEVEN L. SONG, WEIQI SHI, MICHAEL REED. "Accurate Face Rig Approximation with Deep Differential Subspace Reconstruction", TOG(2020). [paper]

  • Gatcluster: Chuang Niu, Jun Zhang, Ge Wang, Jimin Liang. "Gatcluster: Self-supervised gaussian-attention network for image clustering", ECCV(2020) . [paper] [code]

  • KingdraCluster: Divam Gupta, Ramachandran Ramjee, Nipun Kwatra, Muthian Sivathanu. "Unsupervised Clustering using Pseudo-semi-supervised Learning", ICLR(2020) . [project] [Blog] [paper] [code]

  • Hsueh-Ti Derek Liu, Vladimir G. Kim, Siddhartha Chaudhuri, Noam Aigerman, Alec Jacobson. "Neural Subdivision", SIGGRAPH(2020) . [project] [paper] [code]

  • PT: Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, Vladlen Koltun. "Point Transformer", arXiv(2020). [paper] [code_unofficial] ⭐

  • FPCC-Net: Yajun Xu, Shogo Arai, Diyi Liu, Fangzhou Lin, Kazuhiro Kosuge. "FPCC-Net: Fast Point Cloud Clustering for Instance Segmentation", arXiv(2020). [paper]

  • Deep snake: Peng Sida, Jiang Wen, Pi Huaijin, Li Xiuli, Bao Hujun, Zhou Xiaowei. "Deep Snake for Real-Time Instance Segmentation", CVPR(2020) . [paper] [code] ⭐

  • SEG-MAT: Cheng Lin, Lingjie Liu, Changjian Li, Leif Kobbelt, Bin Wang, Shiqing Xin, Wenping Wang. "SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform", TVCG(2020). [paper]

  • MA-Unet: Yutong Cai, Yong Wang. "MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation", arXiv(2020). [paper]

  • Xiangru Huang, Haitao Yang, Etienne Vouga, Qixing Huang. "Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs", NeurIPS(2020) . [paper] [code]

  • SIREN: Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein. "Implicit Neural Representations with Periodic Activation Functions", NeurIPS(2020). [project] [paper] [code] ⭐🔥

  • Jean-Michel Roufosse, Abhishek Sharma, Maks Ovsjanikov. "Unsupervised Deep Learning for Structured Shape Matching", ICCV(2019). [paper] [code] 🔥

  • Yu Wang, Justin Solomon. "Intrinsic and extrinsic operators for shape analysis", Handbook of Numerical Analysis(2019). [paper]

  • CfS-CNN: Ran Song, Yonghuai Liu, Paul L. Rosin. "Mesh Saliency via Weakly Supervised Classification-for-Saliency CNN", TVCG(2019). [paper]

  • NVIDIAGameWorks-kaolin: Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche, Clement Fuji Tsang, Artem Rozantsev, Wenzheng Chen, Tommy Xiang, Rev Lebaredian, Sanja Fidler. "Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research", arXiv(2019) . [project] [paper] [Documentation] ⭐

  • pytorch_geometric: Matthias Fey, Jan Eric Lenssen. "Fast Graph Representation Learning with PyTorch Geometric", arXiv(2019) . [project] [paper] [Documentation] ⭐🔥

  • Graph U-Nets: Hongyang Gao, Shuiwang Ji. "Graph U-Nets", ICML(2019) . [paper] [code] ⭐🔥

  • Xinge Li, Yongjie Jessica Zhang, Xuyang Yang, Haibo Xu, Guoliang Xu. "Point cloud surface segmentation based on volumetric eigenfunctions of the Laplace-Beltrami operator", CAGD(2019). [paper]

  • SE-Net: Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu. "Squeeze-and-Excitation Networks", CVPR(2018). [paper] [code] ⭐🔥

  • Adrien Poulenard, Maks Ovsjanikov. "Multi-directional geodesic neural networks via equivariant convolution", TOG(2018) . [paper]

  • DeepLab3+: Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam. "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation", ECCV(2018). [paper] [code] ⭐🔥

  • Asako Kanezaki. "Unsupervised Image Segmentation by Backpropagation", ICASSP(2018) . [paper] [code] [code_2] ⭐🔥

  • Pvnet: Haoxuan You, Yifan Feng, Rongrong Ji, Yue Gao. "PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition", ACM MM(2018). [paper]

  • UNet++: Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang. "UNet++: A Nested U-Net Architecture for Medical Image Segmentation", DLMIA(2018) . [paper_DLMIA2018] [paper_IEEE TMI] [code] [zhihu] ⭐🔥

  • Deep Functional Maps: Or Litany, Tal Remez, Emanuele Rodolà, Alex M. Bronstein, Michael M. Bronstein. "Deep Functional Maps: Structured Prediction for Dense Shape Correspondence", ICCV(2017). [paper] [code] 🔥

  • OctNet: Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger. "OctNet: Learning Deep 3D Representations at High Resolutions", CVPR(2017). [paper] [code] ⭐🔥

  • Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst. "Geometric Deep Learning: Going beyond Euclidean data ", IEEE Signal Processing Magazine(2017). [paper] 🔥

  • GCN: Thomas N. Kipf, Max Welling. "Semi-Supervised Classification with Graph Convolutional Networks", ICLR(2017) . [paper] [code] ⭐🔥

  • 3D ShapeNets: Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao. "3D ShapeNets: A deep representation for volumetric shapes", CVPR(2015). [paper] 🔥

  • Jonathan Masci, Davide Boscaini, Michael M. Bronstein, Pierre Vandergheynst. "Geodesic Convolutional Neural Networks on Riemannian Manifolds", ICCVW(2015). [paper] 🔥

  • U-Net: Olaf Ronneberger, Philipp Fischer, Thomas Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation", MICCAI(2015) . [project] [paper] [code_non-authors ] ⭐🔥

  • Joan Bruna, Wojciech Zaremba, Arthur D. Szlam, Yann LeCun. "Spectral Networks and Locally Connected Networks on Graphs", CoRR(2014). [paper] 🔥

  • Keenan Crane, Clarisse Weischedel, Max Wardetzky. "Geodesics in heat: A new approach to computing distance based on heat flow", TOG(2013) . [project] [paper] 🔥

  • Functional maps: Maks Ovsjanikov, Mirela Ben-Chen, Justin Solomon, Adrian Butscher, Leonidas Guibas. "Functional Maps: A Flexible Representation of Maps Between Shapes", TOG(2012). [paper] 🔥

  • WKS: Mathieu Aubry, Ulrich Schlickewei, Daniel Cremers. "The wave kernel signature: A quantum mechanical approach to shape analysis", ICCV(2011). [paper] [code_from_SGWS] 🔥

  • Bruno L´evy, Hao (Richard) Zhang. "Spectral Mesh Processing", SIGGRAPH Course(2010). [paper, slides, video] 🔥

  • Shape Google: Maks Ovsjanikov, Alexander M. Bronstein, Michael M. Bronstein, Leonidas J. Guibas. "Shape Google: a computer vision approach to isometry invariant shape retrieval", ICCV(2009). [paper] 🔥

  • HKS: Jian Sun, Maks Ovsjanikov, Leonidas Guibas. "A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion", CGF(2009). [paper] 🔥

  • Hui Huang, Dan Li, Hao Zhang, Uri Ascher, Daniel Cohen-Or. "Consolidation of Unorganized Point Clouds for Surface Reconstruction", SIGGRAPH ASIA(2009). [project] [paper] 🔥

  • Mario Botsch, Olga Sorkine. "On Linear Variational Surface Deformation Methods", TVCG(2008) . [paper] [code] 🔥

  • Ulrike von Luxburg. "A Tutorial on Spectral Clustering", Statistics and Computing(2007). [paper] 🔥

  • Bruno Lévy. "Laplace-Beltrami Eigenfunctions Towards an algorithm that “understands” geometry", SMI(2006) . [paper] 🔥

  • Olga Sorkine. "Differential Representations for Mesh Processing", CGF(2006). [paper] 🔥

  • Olga Sorkine, Daniel Cohen-Or, Dror Irony, Sivan Toledo. "Geometry-aware bases for shape approximation", TVCG(2005). [paper] 🔥

  • Wenyuan Li, Wee-Keong Ng, Ee-Peng Lim. "Spectral Analysis of Text Collection for Similarity-based Clustering", ICDE( 2004) and PAKDD(2004) . [paper_ICDE2004] [paper_PAKDD2004]

  • Mikhail Belkin, Partha Niyogi. "Laplacian Eigenmaps for Dimensionality Reduction and Data Representation", Neural Computation(2003). [paper] 🔥

  • Pierre Alliez, David Cohen-Steiner, Olivier Devillers, Bruno Lévy, Mathieu Desbrun. "Anisotropic Polygonal Remeshing", SIGGRAPH(2003). [paper] 🔥

  • David Cohen-Steiner, Jean-Marie Morvan. "Restricted Delaunay Triangulations and Normal Cycle", SoCG(2003) . [paper] [code_from_mikedh] ⭐🔥

  • Mikhail Belkin, Partha Niyogi. "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering", NIPS(2001) . [paper] 🔥

  • Graph-Cuts: Yuri Y. Boykov, Marie-Pierre Jolly. "Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images", ICCV(2001). [paper] 🔥

  • Zachi Karni, Craig Gotsman. "Spectral Compression of Mesh Geometry", SIGGRAPH(2000) . [paper] 🔥

  • Gabriel Taubin. "A signal processing approach to fair surface design", SIGGRAPH(1995). [paper] 🔥

- Datasets

  • IntrA: Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi. "IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning", CVPR(2020). [paper] [code]

  • RNA molecules: Adrien Poulenard, Marie-Julie Rakotosaona, Yann Ponty, Maks Ovsjanikov. "Effective Rotation-invariant Point CNN with Spherical Harmonics kernels", 3DV(2019). [paper] [code] 🔥

  • Manifold40: Shi-Min Hu, Zheng-Ning Liu, Meng-Hao Guo, Jun-Xiong Cai, Jiahui Huang, Tai-Jiang Mu, Ralph R. Martin. "Subdivision-Based Mesh Convolution Networks", arXiv(2021). [paper] [Dataset] ⭐

  • ModelNet40: Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao. "3D ShapeNets: A Deep Representation for Volumetric Shapes", CVPR(2015). [project] [paper] [code] 🔥

  • PartNet: Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, Hao Su. "PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding", CVPR(2019) . [project] [paper] [video] 🔥

  • CGPart: Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li2, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, Alan Yuille. "CGPart: A Part Segmentation Dataset Based on 3D Computer Graphics Models", arXiv(2021) . [project] [paper]

  • HumanSeg: Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G Kim, and Yaron Lipman. "Convolutional neural networks on surfaces via seamless toric covers", ACM Trans. Graph.(2017) . [paper] [dataset] [dataset_from_MeshCNN] [ground-truth labels on the faces] 🔥

  • ShapeNet: Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, Fisher Yu. "ShapeNet: An Information-Rich 3D Model Repository", arXiv(2015). [project] [paper] 🔥

  • COSEG: Yunhai Wang, Shmulik Asafi, Oliver van Kaick, Hao Zhang, Daniel Cohen-Or, Baoquan Chen. "Active co-analysis of a set of shapes", SIGGRAPH Asia(2012) . [project] [paper] [dataset_from_MeshCNN] [ground-truth labels on the faces] 🔥

  • Cube engraving: Hanocka Rana, Hertz Amir, Fish Noa, Giryes Raja, Fleishman Shachar, Cohen-Or Daniel. "MeshCNN: A Network with an Edge", SIGGRAPH(2019) . [dataset] ⭐🔥

  • PSB: Xiaobai Chen, Aleksey Golovinskiy, Thomas Funkhouser. "A Benchmark for 3D Mesh Segmentation", ACM Transactions on Graphics(2009) . [project] [paper] 🔥

  • ABC: Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo. "ABC: A Big CAD Model Dataset For Geometric Deep Learning", CVPR(2019) . [project] [paper] 🔥

  • Thingi10K: Qingnan Zhou, Alec Jacobson. "Thingi10K: A Dataset of 10,000 3D-Printing Models", arXiv(2016) . [project] [paper] 🔥

  • Philip Shilane, Patrick Min, Michael Kazhdan, Thomas Funkhouser. "The Princeton Shape Benchmark", SMI(2004) . [project] [paper] 🔥

  • Li Yi, Vladimir G. Kim, Duygu Ceylan, I-Chao Shen, Mengyuan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, Leonidas Guibas. "A Scalable Active Framework for Region Annotation in 3D Shape Collections", SIGGRAPH Asia(2016) . [project] [paper] 🔥

  • Three D Scans: Oliver Laric. [project]

  • common-3d-test-models: Alec Jacobson, jmespadero, purvigoel. [github] 🔥

  • SUM: Weixiao Gao, Liangliang Nan, Bas Boom, Hugo Ledoux. "SUM: A Benchmark Dataset of Semantic Urban Meshes", ISPRS Journal of Photogrammetry and Remote Sensing(2021). [project] [paper] [code]

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