mlsanityData to 7 Oct 2026

Tangent Convolutions for Dense Prediction in 3D

arXiv 1807.02443 ↗First posted 6 Jul 2018cs.CVcited by 173

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Abstract (arXiv)

We present an approach to semantic scene analysis using deep convolutional networks. Our approach is based on tangent convolutions - a new construction for convolutional networks on 3D data. In contrast to volumetric approaches, our method operates directly on surface geometry. Crucially, the construction is applicable to unstructured point clouds and other noisy real-world data. We show that tangent convolutions can be evaluated efficiently on large-scale point clouds with millions of points. Using tangent convolutions, we design a deep fully-convolutional network for semantic segmentation of 3D point clouds, and apply it to challenging real-world datasets of indoor and outdoor 3D environments. Experimental results show that the presented approach outperforms other recent deep network constructions in detailed analysis of large 3D scenes.

Key facts. Cited by 173 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2018.

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Citing papers per year: 2021: 51; 2022: 50; 2023: 39; 2024: 19; 2025: 8; 2026: 6.

173 papers.

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  1. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  2. 2022OpenScene: 3D Scene Understanding with Open Vocabulariescited by 556
  3. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 432
  4. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 384
  5. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  6. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  7. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  8. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 166
  9. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 157
  10. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  11. 2023Spherical Transformer for LiDAR-based 3D Recognitioncited by 124
  12. 2025Sonata: Self-Supervised Learning of Reliable Point Representationscited by 108
38 more
  1. 2021Fast Point Transformercited by 103
  2. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  3. 2023Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Trainingcited by 85
  4. 2021Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusioncited by 71
  5. 2024Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategycited by 71
  6. 2022Texturify: Generating Textures on 3D Shape Surfacescited by 65
  7. 20214D Panoptic LiDAR Segmentationcited by 64
  8. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  9. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  10. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  11. 2021Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perceptioncited by 57
  12. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  13. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  14. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  15. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  16. 2021SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Networkcited by 54
  17. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  18. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 49
  19. 2021FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decodingcited by 46
  20. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 42
  21. 2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
  22. 2022Surface Reconstruction from Point Clouds: A Survey and a Benchmarkcited by 41
  23. 2022Meta Architecture for Point Cloud Analysiscited by 39
  24. 2023Attention-based Point Cloud Edge Samplingcited by 37
  25. 2022GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentationcited by 36
  26. 2023Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysiscited by 34
  27. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  28. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  29. 2021ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learningcited by 29
  30. 2021LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignmentcited by 26
  31. 2022Domain generalization of 3D semantic segmentation in autonomous drivingcited by 25
  32. 2021CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Cloudscited by 24
  33. 2023Mask4Former: Mask Transformer for 4D Panoptic Segmentationcited by 24
  34. 2021DRINet++: Efficient Voxel-as-point Point Cloud Segmentationcited by 23
  35. 2022Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregationcited by 23
  36. 2023Clustering based Point Cloud Representation Learning for 3D Analysiscited by 23
  37. 2021DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentationcited by 22
  38. 20223D Vision with Transformers: A Surveycited by 21

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