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SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

arXiv 1803.11527 ↗First posted 30 Mar 2018cs.CVcited by 261

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Abstract (arXiv)

Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds. Towards this we propose a novel convolutional architecture, termed SpiderCNN, to efficiently extract geometric features from point clouds. SpiderCNN is comprised of units called SpiderConv, which extend convolutional operations from regular grids to irregular point sets that can be embedded in R^n, by parametrizing a family of convolutional filters. We design the filter as a product of a simple step function that captures local geodesic information and a Taylor polynomial that ensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical architecture from classical CNNs, which allows it to extract semantic deep features. Experiments on ModelNet40 demonstrate that SpiderCNN achieves state-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive performance on segmentation task.

Key facts. Cited by 261 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2018.

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Citing papers per year: 2021: 73; 2022: 76; 2023: 54; 2024: 31; 2025: 19; 2026: 8.

261 papers.

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  1. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  2. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  3. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 432
  4. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
  5. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  6. 2023OpenMask3D: Open-Vocabulary 3D Instance Segmentationcited by 288
  7. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 262
  8. 2022Language-Grounded Indoor 3D Semantic Segmentation in the Wildcited by 243
  9. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  10. 2021Group-Free 3D Object Detection via Transformerscited by 215
  11. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  12. 2022SoftGroup for 3D Instance Segmentation on Point Cloudscited by 197
38 more
  1. 2021Spatio-temporal Self-Supervised Representation Learning for 3D Point Cloudscited by 180
  2. 2021Frame Averaging for Invariant and Equivariant Network Designcited by 166
  3. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 166
  4. 2022Masked Discrimination for Self-Supervised Learning on Point Cloudscited by 155
  5. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  6. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 143
  7. 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
  8. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 121
  9. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 105
  10. 2021Bidirectional Projection Network for Cross Dimension Scene Understandingcited by 95
  11. 2022POCO: Point Convolution for Surface Reconstructioncited by 91
  12. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 91
  13. 2021Generative Models as Distributions of Functionscited by 89
  14. 2022Surface Representation for Point Cloudscited by 84
  15. 2021Equivariant Point Network for 3D Point Cloud Analysiscited by 81
  16. 2021EfficientLPS: Efficient LiDAR Panoptic Segmentationcited by 76
  17. 2022Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representationscited by 71
  18. 2023Deep Learning on Implicit Neural Representations of Shapescited by 62
  19. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
  20. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  21. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  22. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  23. 2021Style-based Point Generator with Adversarial Rendering for Point Cloud Completioncited by 45
  24. 2023SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentationcited by 44
  25. 2021PatchFormer: An Efficient Point Transformer with Patch Attentioncited by 43
  26. 2023Image as Set of Pointscited by 43
  27. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 42
  28. 2023Self-positioning Point-based Transformer for Point Cloud Understandingcited by 40
  29. 2021Zero-Shot Learning on 3D Point Cloud Objects and Beyondcited by 38
  30. 2021Semantic Segmentation-assisted Scene Completion for LiDAR Point Cloudscited by 37
  31. 2023Attention-based Point Cloud Edge Samplingcited by 37
  32. 2023SAM-guided Graph Cut for 3D Instance Segmentationcited by 35
  33. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  34. 2021Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretrainingcited by 32
  35. 2022A Unified Query-based Paradigm for Point Cloud Understandingcited by 32
  36. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  37. 2022Transformers in 3D Point Clouds: A Surveycited by 31
  38. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30

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