# End-to-end learned Lossy Dynamic Point Cloud Attribute Compression

arXiv: https://arxiv.org/abs/2408.10665 · HTML: https://mlsanity.com/paper/2408.10665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Video Compression With Rate-Distortion Autoencoders](https://mlsanity.com/paper/1908.05717/) (2019)
- [Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model](https://mlsanity.com/paper/2006.13560/) (2020)
- [Sparse Tensor-based Point Cloud Attribute Compression](https://mlsanity.com/paper/2204.01023/) (2022)
- [Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model](https://mlsanity.com/paper/2303.06519/) (2023)
- [Folding-based compression of point cloud attributes](https://mlsanity.com/paper/2002.04439/) (2020)
- [Deep probabilistic model for lossless scalable point cloud attribute compression](https://mlsanity.com/paper/2303.06517/) (2023)
- [4DAC: Learning Attribute Compression for Dynamic Point Clouds](https://mlsanity.com/paper/2204.11723/) (2022)

## Abstract (arXiv)

Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.