# TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement

arXiv: https://arxiv.org/abs/2604.28045 · HTML: https://mlsanity.com/paper/2604.28045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [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)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Multiscale Point Cloud Geometry Compression](https://mlsanity.com/paper/2011.03799/) (2020)
- [Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression](https://mlsanity.com/paper/2111.10633/) (2021)
- [GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression](https://mlsanity.com/paper/2209.04401/) (2022)
- [The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machine](https://mlsanity.com/paper/2409.08130/) (2024)
- [UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach](https://mlsanity.com/paper/2503.18541/) (2025)
- [Deep probabilistic model for lossless scalable point cloud attribute compression](https://mlsanity.com/paper/2303.06517/) (2023)
- [An Overview of the JPEG AI Learning-Based Image Coding Standard](https://mlsanity.com/paper/2510.13867/) (2025)
- [IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud Coding](https://mlsanity.com/paper/2208.02716/) (2022)
- [ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Vision](https://mlsanity.com/paper/2504.14240/) (2025)
- [Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator](https://mlsanity.com/paper/2404.07698/) (2024)
- [Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering](https://mlsanity.com/paper/2406.05915/) (2024)

## Abstract (arXiv)

Scalable compression is essential for bandwidth-adaptive transmission, yet most learned codecs are optimized for a fixed rate-distortion point, making rate adaptation costly due to re-encoding or maintaining multiple bitstreams. In this work, we propose TAFA-GSGC, a scalable learned point cloud geometry codec that enables multi-quality decoding from a single bitstream and a single trained model. TAFA-GSGC combines layered residual refinement with channel-group entropy coding, and introduces a Target-Aligned Feature Aggregation module to reduce cross-layer redundancy in enhancement residuals. Our framework supports up to 9 decodable quality levels with monotonic quality improvement as more subbitstreams are received, while maintaining strong compression efficiency. Compared with the PCGCv2 baseline, TAFA-GSGC demonstrates improved RD performance, achieving average BD-rate reductions of 4.99% and 5.92% in terms of D1-PSNR and D2-PSNR, respectively.