# Graph-based compression of dynamic 3D point cloud sequences

arXiv: https://arxiv.org/abs/1506.06096 · HTML: https://mlsanity.com/paper/1506.06096/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2015.

## Built on by (most cited first, 17 of 17)

- [HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting](https://mlsanity.com/paper/2312.03461/) (2023)
- [Graph Filters for Signal Processing and Machine Learning on Graphs](https://mlsanity.com/paper/2211.08854/) (2022)
- [Inter-Frame Compression for Dynamic Point Cloud Geometry Coding](https://mlsanity.com/paper/2207.12554/) (2022)
- [LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks](https://mlsanity.com/paper/2111.08988/) (2021)
- [Geometric Prior Based Deep Human Point Cloud Geometry Compression](https://mlsanity.com/paper/2305.01309/) (2023)
- [Spatio-temporal Graph-RNN for Point Cloud Prediction](https://mlsanity.com/paper/2102.07482/) (2021)
- [Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding](https://mlsanity.com/paper/2301.12165/) (2023)
- [Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention](https://mlsanity.com/paper/2304.00335/) (2023)
- [Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context](https://mlsanity.com/paper/2503.18283/) (2025)
- [Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression](https://mlsanity.com/paper/2211.10646/) (2022)
- [Fractional Motion Estimation for Point Cloud Compression](https://mlsanity.com/paper/2202.00172/) (2022)
- [4DAC: Learning Attribute Compression for Dynamic Point Clouds](https://mlsanity.com/paper/2204.11723/) (2022)
- [Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction](https://mlsanity.com/paper/2311.13533/) (2023)
- [Motion estimation and filtered prediction for dynamic point cloud attribute compression](https://mlsanity.com/paper/2210.08262/) (2022)
- [Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understanding](https://mlsanity.com/paper/2603.20739/) (2026)
- [SAFT: Sensitivity-Aware Filtering and Transmission for Adaptive 3D Point Cloud Communication over Wireless Channels](https://mlsanity.com/paper/2603.26197/) (2026)
- [Motion Estimation Techniques for Volumetric Video Attribute Compression](https://mlsanity.com/paper/2607.03576/) (2026)

## Abstract (arXiv)

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of these sequences. It however remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the points clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.