# Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

arXiv: https://arxiv.org/abs/2303.06519 · HTML: https://mlsanity.com/paper/2303.06519/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 73 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 25 of 25)

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications](https://mlsanity.com/paper/1701.05517/) (2017)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Conditional Probability Models for Deep Image Compression](https://mlsanity.com/paper/1801.04260/) (2018)
- [Multiscale Point Cloud Geometry Compression](https://mlsanity.com/paper/2011.03799/) (2020)
- [OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression](https://mlsanity.com/paper/2005.07178/) (2020)
- [Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression](https://mlsanity.com/paper/2111.10633/) (2021)
- [OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression](https://mlsanity.com/paper/2202.06028/) (2022)
- [Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression](https://mlsanity.com/paper/1903.08548/) (2019)
- [Improved Deep Point Cloud Geometry Compression](https://mlsanity.com/paper/2006.09043/) (2020)
- [MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models](https://mlsanity.com/paper/2011.07590/) (2020)
- [Generative Image Modeling Using Spatial LSTMs](https://mlsanity.com/paper/1506.03478/) (2015)
- [Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds](https://mlsanity.com/paper/1905.03691/) (2019)
- [Learning-based lossless compression of 3D point cloud geometry](https://mlsanity.com/paper/2011.14700/) (2020)
- [Sparse Tensor-based Point Cloud Attribute Compression](https://mlsanity.com/paper/2204.01023/) (2022)
- [Multiscale deep context modeling for lossless point cloud geometry compression](https://mlsanity.com/paper/2104.09859/) (2021)
- [A Comprehensive Study and Comparison of Core Technologies for MPEG 3D Point Cloud Compression](https://mlsanity.com/paper/1912.09674/) (2019)
- [LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks](https://mlsanity.com/paper/2111.08988/) (2021)
- [Folding-based compression of point cloud attributes](https://mlsanity.com/paper/2002.04439/) (2020)
- [Lossless Coding of Point Cloud Geometry using a Deep Generative Model](https://mlsanity.com/paper/2107.00400/) (2021)
- [Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors](https://mlsanity.com/paper/2204.05043/) (2022)
- [Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds](https://mlsanity.com/paper/2106.06482/) (2021)
- [Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction](https://mlsanity.com/paper/2106.12236/) (2021)

## Built on by (most cited first, 25 of 25)

- [SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds](https://mlsanity.com/paper/2409.10293/) (2024)
- [UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach](https://mlsanity.com/paper/2503.18541/) (2025)
- [Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss](https://mlsanity.com/paper/2407.08520/) (2024)
- [Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction](https://mlsanity.com/paper/2407.08528/) (2024)
- [Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer](https://mlsanity.com/paper/2411.07899/) (2024)
- [Rate-Distortion Optimized Skip Coding of Region Adaptive Hierarchical Transform Coefficients for MPEG G-PCC](https://mlsanity.com/paper/2412.05574/) (2024)
- [ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Vision](https://mlsanity.com/paper/2504.14240/) (2025)
- [3D Point Cloud Network Pruning: When Some Weights Do not Matter](https://mlsanity.com/paper/2408.14601/) (2024)
- [Efficient and Generic Point Model for Lossless Point Cloud Attribute Compression](https://mlsanity.com/paper/2404.06936/) (2024)
- [Diff-PCC: Diffusion-based Neural Compression for 3D Point Clouds](https://mlsanity.com/paper/2408.10543/) (2024)
- [DeepRAHT: Learning Predictive RAHT for Point Cloud Attribute Compression](https://mlsanity.com/paper/2601.12255/) (2026)
- [Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds](https://mlsanity.com/paper/2410.17823/) (2024)
- [Deep-JGAC: End-to-End Deep Joint Geometry and Attribute Compression for Dense Colored Point Clouds](https://mlsanity.com/paper/2502.17939/) (2025)
- [Hierarchical Attention Networks for Lossless Point Cloud Attribute Compression](https://mlsanity.com/paper/2504.00481/) (2025)
- [Efficient LiDAR Reflectance Compression via Scanning Serialization](https://mlsanity.com/paper/2505.09433/) (2025)
- [Point Cloud Compression and Objective Quality Assessment: A Survey](https://mlsanity.com/paper/2506.22902/) (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)
- [Overview and Comparison of AVS Point Cloud Compression Standard](https://mlsanity.com/paper/2602.08613/) (2026)
- [End-to-end learned Lossy Dynamic Point Cloud Attribute Compression](https://mlsanity.com/paper/2408.10665/) (2024)
- [Unified Geometry and Color Compression Framework for Point Clouds via Generative Diffusion Priors](https://mlsanity.com/paper/2503.18083/) (2025)
- [Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2508.01633/) (2025)
- [Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression](https://mlsanity.com/paper/2509.14591/) (2025)
- [Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression](https://mlsanity.com/paper/2605.18006/) (2026)
- [Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression](https://mlsanity.com/paper/2609.34834/) (2026)

## Abstract (arXiv)

In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.