mlsanityData to 14 Oct 2026

SCP: Spherical-Coordinate-based Learned Point Cloud Compression

arXiv 2308.12535 ↗First posted 24 Aug 2023cs.CV eess.IVcited by 12

Not on the map. Why?

Abstract (arXiv)

In recent years, the task of learned point cloud compression has gained prominence. An important type of point cloud, the spinning LiDAR point cloud, is generated by spinning LiDAR on vehicles. This process results in numerous circular shapes and azimuthal angle invariance features within the point clouds. However, these two features have been largely overlooked by previous methodologies. In this paper, we introduce a model-agnostic method called Spherical-Coordinate-based learned Point cloud compression (SCP), designed to leverage the aforementioned features fully. Additionally, we propose a multi-level Octree for SCP to mitigate the reconstruction error for distant areas within the Spherical-coordinate-based Octree. SCP exhibits excellent universality, making it applicable to various learned point cloud compression techniques. Experimental results demonstrate that SCP surpasses previous state-of-the-art methods by up to 29.14% in point-to-point PSNR BD-Rate.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 24 Aug 2023.

Builds on

19 of 34 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 389
  4. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 355
  5. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  6. 2021Checkerboard Context Model for Efficient Learned Image Compressioncited by 202
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  10. 2020OctSqueeze: Octree-Structured Entropy Model for LiDAR Compressioncited by 73
  11. 2021Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compressioncited by 70
  12. 2021VoxelContext-Net: An Octree based Framework for Point Cloud Compressioncited by 61
7 more
  1. 2022OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compressioncited by 58
  2. 2019Learning Convolutional Transforms for Lossy Point Cloud Geometry Compressioncited by 54
  3. 2020MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Modelscited by 42
  4. 2020Learning-based lossless compression of 3D point cloud geometrycited by 26
  5. 2021Deep Point Cloud Reconstructioncited by 15
  6. 2022Point Cloud Compression with Sibling Context and Surface Priorscited by 10
  7. 2021Cylindrical coordinates for LiDAR point cloud compressioncited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 4.

12 papers.

  1. 2025RENO: Real-Time Neural Compression for 3D LiDAR Point Cloudscited by 12
  2. 2025Voxel-based Point Cloud Geometry Compression with Space-to-Channel Contextcited by 4
  3. 2024PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systemscited by 2
  4. 2025Re-Densification Meets Cross-Scale Propagation: Real-Time Neural Compression of LiDAR Point Cloudscited by 2
  5. 2025Point Cloud Compression and Objective Quality Assessment: A Surveycited by 1
  6. 2025AnyPcc: Compressing Any Point Cloud with a Single Universal Modelcited by 1
  7. 2026Towards Practical Lossless Neural Compression for LiDAR Point Cloudscited by 1
  8. 2024Compression of Large-Scale 3D Point Clouds Based on Joint Optimization of Point Sampling and Feature Extractioncited by 0
  9. 2025Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compressioncited by 0
  10. 2026SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representationcited by 0
  11. 2026PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compressioncited by 0
  12. 2026Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compressioncited by 0