# Learning to Predict on Octree for Scalable Point Cloud Geometry Coding

arXiv: https://arxiv.org/abs/2209.02226 · HTML: https://mlsanity.com/paper/2209.02226/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [Learning Neural Volumetric Field for Point Cloud Geometry Compression](https://mlsanity.com/paper/2212.05589/) (2022)
- [Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering](https://mlsanity.com/paper/2406.05915/) (2024)
- [TeSO: Representing and Compressing 3D Point Cloud Scenes with Textured Surfel Octree](https://mlsanity.com/paper/2508.07083/) (2025)

## Abstract (arXiv)

Octree-based point cloud representation and compression have been adopted by the MPEG G-PCC standard. However, it only uses handcrafted methods to predict the probability that a leaf node is non-empty, which is then used for entropy coding. We propose a novel approach for predicting such probabilities for geometry coding, which applies a denoising neural network to a "noisy" context cube that includes both neighboring decoded voxels as well as uncoded voxels. We further propose a convolution-based model to upsample the decoded point cloud at a coarse resolution on the decoder side. Integration of the two approaches significantly improves the rate-distortion performance for geometry coding compared to the original G-PCC standard and other baseline methods for dense point clouds. The proposed octree-based entropy coding approach is naturally scalable, which is desirable for dynamic rate adaptation in point cloud streaming systems.