mlsanityData to 14 Oct 2026

RWKV-PCSSC: Exploring RWKV Model for Point Cloud Semantic Scene Completion

arXiv 2511.09878 ↗First posted 13 Nov 2025cs.CVcited by 1

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Abstract (arXiv)

Semantic Scene Completion (SSC) aims to generate a complete semantic scene from an incomplete input. Existing approaches often employ dense network architectures with a high parameter count, leading to increased model complexity and resource demands. To address these limitations, we propose RWKV-PCSSC, a lightweight point cloud semantic scene completion network inspired by the Receptance Weighted Key Value (RWKV) mechanism. Specifically, we introduce a RWKV Seed Generator (RWKV-SG) module that can aggregate features from a partial point cloud to produce a coarse point cloud with coarse features. Subsequently, the point-wise feature of the point cloud is progressively restored through multiple stages of the RWKV Point Deconvolution (RWKV-PD) modules. By leveraging a compact and efficient design, our method achieves a lightweight model representation. Experimental results demonstrate that RWKV-PCSSC reduces the parameter count by 4.18× and improves memory efficiency by 1.37× compared to state-of-the-art methods PointSSC. Furthermore, our network achieves state-of-the-art performance on established indoor (SSC-PC, NYUCAD-PC) and outdoor (PointSSC) scene dataset, as well as on our proposed datasets (NYUCAD-PC-V2, 3D-FRONT-PC).

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 39 of its 59 references that are in the map. First posted on arXiv 13 Nov 2025.

Builds on

39 of 59 references.

References · 39

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2020Point Transformercited by 1,470
  5. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  6. 2023RWKV: Reinventing RNNs for the Transformer Eracited by 827
  7. 2020PCT: Point cloud transformercited by 732
  8. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  9. 2016Semantic Scene Completion from a Single Depth Imagecited by 472
  10. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  11. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 380
  12. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
27 more
  1. 20203D-FUTURE: 3D Furniture shape with TextUREcited by 311
  2. 2017Cognitive Mapping and Planning for Visual Navigationcited by 212
  3. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  4. 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 167
  5. 2019Morphing and Sampling Network for Dense Point Cloud Completioncited by 132
  6. 2020Anisotropic Convolutional Networks for 3D Semantic Scene Completioncited by 127
  7. 20203D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Priorcited by 126
  8. 2024Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architecturescited by 98
  9. 2020PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Pathscited by 97
  10. 2022SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformercited by 83
  11. 2018RGCNN: Regularized Graph CNN for Point Cloud Segmentationcited by 70
  12. 2023AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformerscited by 67
  13. 2019Efficient Semantic Scene Completion Network with Spatial Group Convolutioncited by 66
  14. 2022PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Pathscited by 57
  15. 2024Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Modelcited by 57
  16. 2020SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigationcited by 44
  17. 2024Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Modelscited by 41
  18. 2022Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformercited by 38
  19. 2024CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformerscited by 21
  20. 2024PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learningcited by 21
  21. 2024KPConvX: Modernizing Kernel Point Convolution with Kernel Attentioncited by 16
  22. 2019EdgeNet: Semantic Scene Completion from a Single RGB-D Imagecited by 13
  23. 2023Semantic Scene Completion with Cleaner Selfcited by 12
  24. 2022CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusioncited by 11
  25. 2015Predicting Complete 3D Models of Indoor Scenescited by 9
  26. 2021Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priorscited by 9
  27. 2024OccRWKV: Rethinking Efficient 3D Semantic Occupancy Prediction with Linear Complexitycited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Compass: Degradation-Simulated Reciprocal Learning with Lightweight Needle RWKV for Multimodal Crack Segmentation under Missing Modalitiescited by 0