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Recurrent Slice Networks for 3D Segmentation of Point Clouds

arXiv 1802.04402 ↗First posted 13 Feb 2018cs.CVcited by 82

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Abstract (arXiv)

Point clouds are an efficient data format for 3D data. However, existing 3D segmentation methods for point clouds either do not model local dependencies citepointnet or require added computations citekd-net,pointnet2. This work presents a novel 3D segmentation framework, RSNetfootnoteCodes are released here https://github.com/qianguih/RSNet, to efficiently model local structures in point clouds. The key component of the RSNet is a lightweight local dependency module. It is a combination of a novel slice pooling layer, Recurrent Neural Network (RNN) layers, and a slice unpooling layer. The slice pooling layer is designed to project features of unordered points onto an ordered sequence of feature vectors so that traditional end-to-end learning algorithms (RNNs) can be applied. The performance of RSNet is validated by comprehensive experiments on the S3DIScitestanford, ScanNetcitescannet, and ShapeNet citeshapenet datasets. In its simplest form, RSNets surpass all previous state-of-the-art methods on these benchmarks. And comparisons against previous state-of-the-art methods citepointnet, pointnet2 demonstrate the efficiency of RSNets.

Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2018.

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Citing papers per year: 2021: 26; 2022: 18; 2023: 18; 2024: 14; 2025: 5; 2026: 1.

82 papers.

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  1. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  2. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  3. 2022PLA: Language-Driven Open-Vocabulary 3D Scene Understandingcited by 176
  4. 2021Mix3D: Out-of-Context Data Augmentation for 3D Scenescited by 126
  5. 2021PointMixer: MLP-Mixer for Point Cloud Understandingcited by 71
  6. 20223D Object Detection from Images for Autonomous Driving: A Surveycited by 66
  7. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  8. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
  9. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  10. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  11. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  12. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
38 more
  1. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  2. 2021PatchFormer: An Efficient Point Transformer with Patch Attentioncited by 43
  3. 2024OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentationcited by 43
  4. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 42
  5. 2023Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understandingcited by 38
  6. 2024FastLGS: Speeding up Language Embedded Gaussians with Feature Grid Mappingcited by 34
  7. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  8. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  9. 2022Weakly Supervised Semantic Segmentation for Large-Scale Point Cloudcited by 27
  10. 2023Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentationcited by 26
  11. 2023Interactive Segment Anything NeRF with Feature Imitationcited by 24
  12. 2023Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlookcited by 24
  13. 2023Clustering based Point Cloud Representation Learning for 3D Analysiscited by 23
  14. 2021Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoningcited by 22
  15. 2021DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentationcited by 22
  16. 2021Spatial-Temporal Transformer for 3D Point Cloud Sequencescited by 22
  17. 2024An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Modelscited by 22
  18. 2022PVNAS: 3D Neural Architecture Search with Point-Voxel Convolutioncited by 16
  19. 2021PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Featurescited by 15
  20. 2024ChatCam: Empowering Camera Control through Conversational AIcited by 15
  21. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  22. 2021FatNet: A Feature-attentive Network for 3D Point Cloud Processingcited by 10
  23. 2022CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learningcited by 9
  24. 2024PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Modelcited by 9
  25. 2022LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsamplingcited by 8
  26. 2021DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Cloudscited by 7
  27. 2021Continuous Conditional Random Field Convolution for Point Cloud Segmentationcited by 7
  28. 2024Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Samplingcited by 7
  29. 2021Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloudcited by 6
  30. 2022PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stagecited by 6
  31. 2023Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentationcited by 6
  32. 2024Localization and Expansion: A Decoupled Framework for Point Cloud Few-shot Semantic Segmentationcited by 6
  33. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  34. 2022AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perceptioncited by 5
  35. 2022LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLADcited by 5
  36. 2022PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priorscited by 5
  37. 2023Learning from Mistakes: Self-Regularizing Hierarchical Representations in Point Cloud Semantic Segmentationcited by 5
  38. 2024RISurConv: Rotation Invariant Surface Attention-Augmented Convolutions for 3D Point Cloud Classification and Segmentationcited by 5

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