# Deep probabilistic model for lossless scalable point cloud attribute compression

arXiv: https://arxiv.org/abs/2303.06517 · HTML: https://mlsanity.com/paper/2303.06517/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications](https://mlsanity.com/paper/1701.05517/) (2017)
- [Sparse Tensor-based Point Cloud Attribute Compression](https://mlsanity.com/paper/2204.01023/) (2022)
- [Folding-based compression of point cloud attributes](https://mlsanity.com/paper/2002.04439/) (2020)
- [Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction](https://mlsanity.com/paper/2106.12236/) (2021)

## Built on by (most cited first, 7 of 7)

- [Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression](https://mlsanity.com/paper/2404.06936/) (2024)
- [Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds](https://mlsanity.com/paper/2410.17823/) (2024)
- [Efficient LiDAR Reflectance Compression via Scanning Serialization](https://mlsanity.com/paper/2505.09433/) (2025)
- [UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds](https://mlsanity.com/paper/2510.23009/) (2025)
- [DALD-PCAC: Density-Adaptive Learning Descriptor for Point Cloud Lossless Attribute Compression](https://mlsanity.com/paper/2601.12261/) (2026)
- [End-to-end learned Lossy Dynamic Point Cloud Attribute Compression](https://mlsanity.com/paper/2408.10665/) (2024)
- [TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement](https://mlsanity.com/paper/2604.28045/) (2026)

## Abstract (arXiv)

In recent years, several point cloud geometry compression methods that utilize advanced deep learning techniques have been proposed, but there are limited works on attribute compression, especially lossless compression. In this work, we build an end-to-end multiscale point cloud attribute coding method (MNeT) that progressively projects the attributes onto multiscale latent spaces. The multiscale architecture provides an accurate context for the attribute probability modeling and thus minimizes the coding bitrate with a single network prediction. Besides, our method allows scalable coding that lower quality versions can be easily extracted from the losslessly compressed bitstream. We validate our method on a set of point clouds from MVUB and MPEG and show that our method outperforms recently proposed methods and on par with the latest G-PCC version 14. Besides, our coding time is substantially faster than G-PCC.