# Predictive Generalized Graph Fourier Transform for Attribute Compression of Dynamic Point Clouds

arXiv: https://arxiv.org/abs/1908.01970 · HTML: https://mlsanity.com/paper/1908.01970/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks](https://mlsanity.com/paper/2202.07261/) (2022)
- [Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing](https://mlsanity.com/paper/2207.13326/) (2022)
- [SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds](https://mlsanity.com/paper/2409.10293/) (2024)
- [PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features](https://mlsanity.com/paper/2403.01804/) (2024)
- [Fractional Motion Estimation for Point Cloud Compression](https://mlsanity.com/paper/2202.00172/) (2022)
- [Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression](https://mlsanity.com/paper/2404.06936/) (2024)
- [DeepRAHT: Learning Predictive RAHT for Point Cloud Attribute Compression](https://mlsanity.com/paper/2601.12255/) (2026)
- [Motion estimation and filtered prediction for dynamic point cloud attribute compression](https://mlsanity.com/paper/2210.08262/) (2022)
- [DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression](https://mlsanity.com/paper/2601.12261/) (2026)
- [Motion Estimation Techniques for Volumetric Video Attribute Compression](https://mlsanity.com/paper/2607.03576/) (2026)
- [Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression](https://mlsanity.com/paper/2609.34834/) (2026)

## Abstract (arXiv)

As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields such as immersive telepresence, navigation for autonomous driving and gaming. Nevertheless, the tremendous amount of data in dynamic point clouds significantly burden transmission and storage. To this end, we propose a complete compression framework for attributes of 3D dynamic point clouds, focusing on optimal inter-coding. Firstly, we derive the optimal inter-prediction and predictive transform coding assuming the Gaussian Markov Random Field model with respect to a spatio-temporal graph underlying the attributes of dynamic point clouds. The optimal predictive transform proves to be the Generalized Graph Fourier Transform in terms of spatio-temporal decorrelation. Secondly, we propose refined motion estimation via efficient registration prior to inter-prediction, which searches the temporal correspondence between adjacent frames of irregular point clouds. Finally, we present a complete framework based on the optimal inter-coding and our previously proposed intra-coding, where we determine the optimal coding mode from rate-distortion optimization with the proposed offline-trained λ-Q model. Experimental results show that we achieve around 17% bit rate reduction on average over competitive dynamic point cloud compression methods.