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: :star: code is available & stars >= 100  |  :fire: citation >= 50

- Topics

• Mesh Feature Extraction or Mesh segmentation
• Datasets
• Courses
• Paper Resources
• Following

Feature Extraction of Meshes or Mesh segmentation

2023

• BRUNO ROY. "Neural Shape Diameter Function for Efficient Mesh Segmentation", SIGGRAPH(2023). [paper]

• MWFormer: Haoyang peng, Meng-Hao Guo, Zheng-Ning Liu, Yong-Liang Yang, Tai-Jiang Mu. "MWFormer: Mesh Understanding with Window-Based Transformer", SSRN(2023). [paper]

• Picasso++: Huan Lei, Naveed Akhtar, Mubarak Shah, Ajmal Mian. "Mesh Convolution with Continuous Filters for 3D Surface Parsing", TNNLS(2023). [paper] [code] :star:

• DGNet: Xiang-Li Li, Zheng-Ning Liu, Tuo Chen, Tai-Jiang Mu, Ralph R. Martin, Shi-Min Hu. "Mesh Neural Networks Based on Dual Graph Pyramids", TVCG(2023). [paper] [code]

2022

• Laplacian2Mesh: Qiujie Dong, Zixiong Wang, Manyi Li, Junjie Gao, Shuangmin Chen, Zhenyu Shu, Shiqing Xin, Changhe Tu, Wenping Wang. "Laplacian2Mesh: Laplacian-Based Mesh Understanding", TVCG( 2023). [paper] [code]

• MeshFormers: Hao-Yang Peng, Meng-Hao Guo, Zheng-Ning Liu, Yong-Liang Yang, Tai-Jiang Mu. "MeshFormers: Transformer-Based Networks for Mesh Understanding", SSRN(2022). [paper]

• MeshFormer: Yuan Li, Xiangyang He, Yankai Jiang, Huan Liu, Yubo Tao, Lin Hai. "MeshFormer: High-resolution Mesh Segmentation with Graph Transformer", CGF(2022). [paper]

• 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]

• MeshNet++: Vinit Veerendraveer Singh, Shivanand Venkanna Sheshappanavar, Chandra Kambhamettu. "MeshNet++: A Network with a Face", ACM MM(2021). [paper]

2020

• Long Zhang, Jianwei Guo, Jun Xiao, Xiaopeng Zhang, Dong-Ming Yan. "Blending Surface Segmentation and Editing for 3D Models", TVCG(2020). [paper]

• 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] :star:

• 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] :star::fire:

• 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] :star::fire:

• 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

• MDGCNN: ADRIEN POULENARD, MAKS OVSJANIKOV. "Multi-directional Geodesic Neural Networks via Equivariant Convolution", TOG(2018). [paper] [code] :fire:

• 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] :fire:

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

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

• 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] :star::fire:

• 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] :fire:

• 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] :fire:

• 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] :fire:

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

• OLIVER VAN KAICK, NOA FISH, YANIR KLEIMAN, SHMUEL ASAFI, DANIEL COHEN-OR. "Shape Segmentation by Approximate Convexity Analysis", TOG(2014). [paper] :fire:

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

• 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] :fire:

• 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] :fire:

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

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

• 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] :fire:

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

• Shapira Lior, Shamir Ariel, Cohen-Or Daniel. "Consistent mesh partitioning and skeletonisation using the shape diameter function", TVC(2008). [paper] :fire:

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

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

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

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

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

Related works

• GrowSP: Zihui Zhang, Bo Yang, Bing Wang, Bo Li. "GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds", CVPR(2023). [paper] [code]

• SHRED: R. KENNY JONES, AALIA HABIB, DANIEL RITCHIE. "SHRED: 3D Shape Region Decomposition with Learned Local Operations", Siggraph Asia( 2022). [project] [paper] [code]

• 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] :star:

• 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] :star:

• 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] :star::fire:

• 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] :star::fire:

• 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] :star:

• 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] :star:

• 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] :star::fire:

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

• 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] :star:

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

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

• 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] :star::fire:

• 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] :star::fire:

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

• 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] :star::fire:

• 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] :fire:

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

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

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

• 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] :fire:

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

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

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

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

• 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] :fire:

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

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

• 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] :fire:

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

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

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

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

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

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

• Olga Sorkine. "Laplacian Mesh Processing", EG(2005). [paper] :fire:

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

• 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] :fire:

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

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

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

• 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] :fire:

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

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

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] :fire:

• 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] :star:

• 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] :fire:

• 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] :fire:

• 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] :fire:

• 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] :fire:

• 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] :fire:

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

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

• 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] :fire:

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

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

• 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] :fire:

• Three D Scans: Oliver Laric. [project]

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

• 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]

Courses

• Discrete Differential Geometry: Keenan Crane
• Machine Learning: Hung-yi Lee
• Digital Geometry Processing: Maks Ovsjanikov
• 数字几何处理: Xiao-Ming Fu
• GAMES102: Ligang Liu
• GAMES_Summer 2021: Qixing Huang

Paper Resources

• Conference Papers for CG: Ke-Sen Huang
• Conference Papers for CV: GAP Lab

Following

• Shiqing Xin
• Rana Hanocka
• Maks Ovsjanikov
• Keenan Crane
• Justin Solomon
• Nicholas Sharp
• Hsueh-Ti Derek Liu
• Interactive Geometry Lab(IGL) from ETH Zurich
• THOMAS KIPF
• Xiaoguang Han
• Ligang Liu
• Xiao-Ming Fu