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A Task-driven Network for Mesh Classification and Semantic Part Segmentation

arXiv 2306.05246 ↗First posted 8 Jun 2023cs.CV cs.GRcited by 0

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Abstract (arXiv)

With the rapid development of geometric deep learning techniques, many mesh-based convolutional operators have been proposed to bridge irregular mesh structures and popular backbone networks. In this paper, we show that while convolutions are helpful, a simple architecture based exclusively on multi-layer perceptrons (MLPs) is competent enough to deal with mesh classification and semantic segmentation. Our new network architecture, named Mesh-MLP, takes mesh vertices equipped with the heat kernel signature (HKS) and dihedral angles as the input, replaces the convolution module of a ResNet with Multi-layer Perceptron (MLP), and utilizes layer normalization (LN) to perform the normalization of the layers. The all-MLP architecture operates in an end-to-end fashion and does not include a pooling module. Extensive experimental results on the mesh classification/segmentation tasks validate the effectiveness of the all-MLP architecture.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 39 of its 63 references that are in the map. First posted on arXiv 8 Jun 2023.

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39 of 63 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2016Layer Normalizationcited by 5,669
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  6. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  7. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  8. 2018Group Normalizationcited by 1,524
  9. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
  10. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  11. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  12. 2020PCT: Point cloud transformercited by 732
27 more
  1. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 712
  2. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  3. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  4. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 342
  5. 2018SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filterscited by 261
  6. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  7. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  8. 2020DiffusionNet: Discretization Agnostic Learning on Surfacescited by 164
  9. 20183D-CODED : 3D Correspondences by Deep Deformationcited by 160
  10. 2017Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Modelscited by 142
  11. 2017Deep Functional Maps: Structured Prediction for Dense Shape Correspondencecited by 134
  12. 2017VNect: Real-time 3D Human Pose Estimation with a Single RGB Cameracited by 129
  13. 2017Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Modelscited by 128
  14. 2020Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondencecited by 125
  15. 2018MeshNet: Mesh Neural Network for 3D Shape Representationcited by 95
  16. 2018Unsupervised Deep Learning for Structured Shape Matchingcited by 81
  17. 2018Continuous and Orientation-preserving Correspondences via Functional Mapscited by 77
  18. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  19. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
  20. 2020Primal-Dual Mesh Convolutional Neural Networkscited by 44
  21. 2020CNNs on Surfaces using Rotation-Equivariant Featurescited by 43
  22. 2020IntrA: 3D Intracranial Aneurysm Dataset for Deep Learningcited by 41
  23. 2019Geometry Sharing Network for 3D Point Cloud Classification and Segmentationcited by 33
  24. 2018Multi-directional Geodesic Neural Networks via Equivariant Convolutioncited by 32
  25. 20213D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysiscited by 23
  26. 2020A Survey on Deep Geometry Learning: From a Representation Perspectivecited by 19
  27. 2022Laplacian2Mesh: Laplacian-Based Mesh Understandingcited by 11

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