# Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression

arXiv: https://arxiv.org/abs/1903.08548 · HTML: https://mlsanity.com/paper/1903.08548/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2019.

## Built on by (most cited first, 30 of 54)

- [An Introduction to Neural Data Compression](https://mlsanity.com/paper/2202.06533/) (2022)
- [Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression](https://mlsanity.com/paper/2111.10633/) (2021)
- [HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting](https://mlsanity.com/paper/2312.03461/) (2023)
- [OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression](https://mlsanity.com/paper/2202.06028/) (2022)
- [Density-preserving Deep Point Cloud Compression](https://mlsanity.com/paper/2204.12684/) (2022)
- [HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media](https://mlsanity.com/paper/2110.07235/) (2021)
- [Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model](https://mlsanity.com/paper/2303.06519/) (2023)
- [3DAC: Learning Attribute Compression for Point Clouds](https://mlsanity.com/paper/2203.09931/) (2022)
- [Multiscale deep context modeling for lossless point cloud geometry compression](https://mlsanity.com/paper/2104.09859/) (2021)
- [Inter-Frame Compression for Dynamic Point Cloud Geometry Coding](https://mlsanity.com/paper/2207.12554/) (2022)
- [LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks](https://mlsanity.com/paper/2111.08988/) (2021)
- [GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression](https://mlsanity.com/paper/2209.04401/) (2022)
- [Patch-Based Deep Autoencoder for Point Cloud Geometry Compression](https://mlsanity.com/paper/2110.09109/) (2021)
- [Attribute Artifacts Removal for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2112.00560/) (2021)
- [SCP: Spherical-Coordinate-based Learned Point Cloud Compression](https://mlsanity.com/paper/2308.12535/) (2023)
- [4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming](https://mlsanity.com/paper/2509.17513/) (2025)
- [Lossless Coding of Point Cloud Geometry using a Deep Generative Model](https://mlsanity.com/paper/2107.00400/) (2021)
- [Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression](https://mlsanity.com/paper/2209.12512/) (2022)
- [Geometric Prior Based Deep Human Point Cloud Geometry Compression](https://mlsanity.com/paper/2305.01309/) (2023)
- [The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machine](https://mlsanity.com/paper/2409.08130/) (2024)
- [IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression](https://mlsanity.com/paper/2208.02519/) (2022)
- [ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression](https://mlsanity.com/paper/2211.10916/) (2022)
- [PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression](https://mlsanity.com/paper/2402.07243/) (2024)
- [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)
- [Variable Rate Compression for Raw 3D Point Clouds](https://mlsanity.com/paper/2202.13862/) (2022)
- [Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention](https://mlsanity.com/paper/2304.00335/) (2023)
- [Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context](https://mlsanity.com/paper/2503.18283/) (2025)
- [ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Vision](https://mlsanity.com/paper/2504.14240/) (2025)
- [Point Cloud Compression via Constrained Optimal Transport](https://mlsanity.com/paper/2403.08236/) (2024)

## Abstract (arXiv)

Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions. In this paper, we present a novel data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization. We perform joint optimization of both rate and distortion using a trade-off parameter. In addition, we cast the decoding process as a binary classification of the point cloud occupancy map. Our method outperforms the MPEG reference solution in terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset with 51.5% BDBR savings on average. Moreover, while octree-based methods face exponential diminution of the number of points at low bitrates, our method still produces high resolution outputs even at low bitrates. Code and supplementary material are available at https://github.com/mauriceqch/pcc_geo_cnn .