# Learning-based lossless compression of 3D point cloud geometry

arXiv: https://arxiv.org/abs/2011.14700 · HTML: https://mlsanity.com/paper/2011.14700/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 26 of 26)

- [OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression](https://mlsanity.com/paper/2202.06028/) (2022)
- [Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model](https://mlsanity.com/paper/2303.06519/) (2023)
- [Multiscale deep context modeling for lossless point cloud geometry compression](https://mlsanity.com/paper/2104.09859/) (2021)
- [D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction](https://mlsanity.com/paper/2205.01135/) (2022)
- [SCP: Spherical-Coordinate-based Learned Point Cloud Compression](https://mlsanity.com/paper/2308.12535/) (2023)
- [Geometric Prior Based Deep Human Point Cloud Geometry Compression](https://mlsanity.com/paper/2305.01309/) (2023)
- [ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression](https://mlsanity.com/paper/2211.10916/) (2022)
- [UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach](https://mlsanity.com/paper/2503.18541/) (2025)
- [Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression](https://mlsanity.com/paper/2402.11250/) (2024)
- [Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss](https://mlsanity.com/paper/2407.08520/) (2024)
- [Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds](https://mlsanity.com/paper/2106.06482/) (2021)
- [PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems](https://mlsanity.com/paper/2405.01750/) (2024)
- [Event-based Solutions for Human-centered Applications: A Comprehensive Review](https://mlsanity.com/paper/2502.18490/) (2025)
- [Point Cloud Compression with Bits-back Coding](https://mlsanity.com/paper/2410.18115/) (2024)
- [DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds](https://mlsanity.com/paper/2411.13860/) (2024)
- [AnyPcc: Compressing Any Point Cloud with a Single Universal Model](https://mlsanity.com/paper/2510.20331/) (2025)
- [Foundry: Distilling 3D Foundation Models for the Edge](https://mlsanity.com/paper/2511.20721/) (2025)
- [DeepCompress: Efficient Point Cloud Geometry Compression](https://mlsanity.com/paper/2106.01504/) (2021)
- [Lossless Compression of Point Cloud Sequences Using Sequence Optimized CNN Models](https://mlsanity.com/paper/2206.01297/) (2022)
- [Lightweight super resolution network for point cloud geometry compression](https://mlsanity.com/paper/2311.00970/) (2023)
- [PVContext: Hybrid Context Model for Point Cloud Compression](https://mlsanity.com/paper/2409.12724/) (2024)
- [Unified Geometry and Color Compression Framework for Point Clouds via Generative Diffusion Priors](https://mlsanity.com/paper/2503.18083/) (2025)
- [FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds](https://mlsanity.com/paper/2511.20065/) (2025)
- [SurfelSoup: Learned Point Cloud Geometry Compression With a Probablistic SurfelTree Representation](https://mlsanity.com/paper/2602.00186/) (2026)
- [Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression](https://mlsanity.com/paper/2605.18006/) (2026)
- [OctMesh: A Unified Octree-Hierarchical Framework for Lossless Triangle Mesh Compression](https://mlsanity.com/paper/2610.04281/) (2026)

## Abstract (arXiv)

This paper presents a learning-based, lossless compression method for static point cloud geometry, based on context-adaptive arithmetic coding. Unlike most existing methods working in the octree domain, our encoder operates in a hybrid mode, mixing octree and voxel-based coding. We adaptively partition the point cloud into multi-resolution voxel blocks according to the point cloud structure, and use octree to signal the partitioning. On the one hand, octree representation can eliminate the sparsity in the point cloud. On the other hand, in the voxel domain, convolutions can be naturally expressed, and geometric information (i.e., planes, surfaces, etc.) is explicitly processed by a neural network. Our context model benefits from these properties and learns a probability distribution of the voxels using a deep convolutional neural network with masked filters, called VoxelDNN. Experiments show that our method outperforms the state-of-the-art MPEG G-PCC standard with average rate savings of 28% on a diverse set of point clouds from the Microsoft Voxelized Upper Bodies (MVUB) and MPEG. The implementation is available at https://github.com/Weafre/VoxelDNN.