# Codec-Compatible Learned Compression for 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2610.26920 · HTML: https://mlsanity.com/paper/2610.26920/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in 3D Gaussian splatting methods, one of 15 directions in 3D reconstruction and neural rendering.
- Builds on 26 of its 32 references that are in the map.
- First posted on arXiv 17 Oct 2026.

## Placed in

- 3D reconstruction and neural rendering / [3D Gaussian splatting methods](https://mlsanity.com/recon3d/3d-gaussian-splatting-methods/)

## Builds on (most cited first, 26 of 26)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://mlsanity.com/paper/2312.16256/) (2023)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis](https://mlsanity.com/paper/2401.02436/) (2023)
- [HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression](https://mlsanity.com/paper/2403.14530/) (2024)
- [Compact 3D Scene Representation via Self-Organizing Gaussian Grids](https://mlsanity.com/paper/2312.13299/) (2023)
- [CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization](https://mlsanity.com/paper/2311.18159/) (2023)
- [ContextGS: Compact 3D Gaussian Splatting with Anchor Level Context Model](https://mlsanity.com/paper/2405.20721/) (2024)
- [PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting](https://mlsanity.com/paper/2406.10219/) (2024)
- [MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation](https://mlsanity.com/paper/2409.09756/) (2024)
- [Fast Feedforward 3D Gaussian Splatting Compression](https://mlsanity.com/paper/2410.08017/) (2024)
- [CAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression](https://mlsanity.com/paper/2503.00357/) (2025)
- [Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs](https://mlsanity.com/paper/2501.03399/) (2025)
- [DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks](https://mlsanity.com/paper/1907.11900/) (2019)
- [A Hierarchical Compression Technique for 3D Gaussian Splatting Compression](https://mlsanity.com/paper/2411.06976/) (2024)
- [GSCodec Studio: A Modular Framework for Gaussian Splat Compression](https://mlsanity.com/paper/2506.01822/) (2025)
- [HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder](https://mlsanity.com/paper/2505.01938/) (2025)
- [Adaptive Voxelization for Transform coding of 3D Gaussian splatting data](https://mlsanity.com/paper/2506.00271/) (2025)
- [Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers](https://mlsanity.com/paper/2402.05887/) (2024)
- [3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression](https://mlsanity.com/paper/2508.07038/) (2025)
- [Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling](https://mlsanity.com/paper/2512.00877/) (2025)
- [Lightweight 3D Gaussian Splatting Compression via Video Codec](https://mlsanity.com/paper/2512.11186/) (2025)
- [Structured Image-based Coding for Efficient Gaussian Splatting Compression](https://mlsanity.com/paper/2601.14510/) (2026)

## Abstract (arXiv)

Video-based 3D Gaussian Splatting (3DGS) compression methods provide a practical route by representing Gaussian attributes as 2D maps and coding them with standard video codecs. Among these maps, high-dimensional non-DC spherical harmonics (SH-AC) maps contain the largest number of channels and account for a dominant portion of the bitstream. However, their statistics differ substantially from those of natural images, and the nonlinear dependencies among SH-AC coefficients remain insufficiently exploited. To address these issues, we propose CCT-GS, a Codec-Compatible learned Transform framework for 3DGS compression. CCT-GS first learns a generalized SH-AC transform that captures cross-scene regularities and converts SH-AC maps into compact codec-friendly bottleneck maps. It further introduces LoRA-based scene adaptation to recover scene-specific appearance details with lightweight side information. Experiments on MPEG GSC sequences demonstrate that CCT-GS consistently outperforms representative video-based baselines, achieving an average BD-rate reduction of 51.30% over the video-based anchor.