mlsanityData to 14 Oct 2026

A Comprehensive Study and Comparison of Core Technologies for MPEG 3D Point Cloud Compression

arXiv 1912.09674 ↗First posted 20 Dec 2019eess.IV cs.CVcited by 19

Not on the map. Why?

Abstract (arXiv)

Point cloud based 3D visual representation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 1; 2024: 6; 2025: 4; 2026: 2.

19 papers.

Sort:

  1. 2023Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Modelcited by 25
  2. 2024SPAC: Sampling-based Progressive Attribute Compression for Dense Point Cloudscited by 11
  3. 2024Enhancing context models for point cloud geometry compression with context feature residuals and multi-losscited by 5
  4. 2025High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCCcited by 5
  5. 2022Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Cloudscited by 4
  6. 2024Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformercited by 4
  7. 2024Rate-Distortion Optimized Skip Coding of Region Adaptive Hierarchical Transform Coefficients for MPEG G-PCCcited by 4
  8. 2022Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compressioncited by 3
  9. 2024Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAVcited by 2
  10. 2024Implicit Neural Compression of Point Cloudscited by 1
  11. 2025Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Codingcited by 1
  12. 2021Enabling 3D Object Detection with a Low-Resolution LiDARcited by 0
7 more
  1. 2022Dynamic Point Cloud Compression with Cross-Sectional Approachcited by 0
  2. 2022Lossless Compression of Point Cloud Sequences Using Sequence Optimized CNN Modelscited by 0
  3. 2022Efficient dynamic point cloud coding using Slice-Wise Segmentationcited by 0
  4. 2025Squeeze3D: Extreme Neural Compression with Latent Space Bridgingcited by 0
  5. 2025Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compressioncited by 0
  6. 2026Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compressioncited by 0
  7. 2026Robust, Estimator-Agnostic Dynamic 3DGS Compressioncited by 0