3D Gaussian Splatting (3DGS) has recently emerged as a promising 3D representation. However, its substantial storage requirements necessitate efficient compression for transmission and application. This work introduces a novel rate-distortion-optimized compression framework for 3DGS, termed CAT-3DGS. We apply masking and coding techniques within ScaffoldGS to ensure efficient data transmission. For coding, we utilize the triplane hyperprior and employ channel-wise autoregressive models to predict probabilities for entropy coding. Moreover, an improved masking mechanism further enhances efficieny.
Combined with these features, CAT-3DGS achieves state-of-the-art compression performance on real-world datasets. On the Mip-NeRF 360 dataset, our CAT-3DGS achieves (at its second highest rate point) 78× and 26x rate reductions than 3DGS and ScaffoldGS, respectively, while achieving slightly higher PSNR by 0.16 dB.
Our work, CAT-3DGS, introduces a rate-distortion-optimized approach that leverages context-adaptive triplanes to improve compression efficiency. By aligning multi-scale triplanes with the principal axes of Gaussian primitives, we capture spatial correlations for spatial autoregressive coding (e), while channel-wise autoregressive coding (d) exploits intra dependencies within each individual Gaussian primitive. A view frequency-aware masking mechanism further refines the process by skipping primitives with minimal impact on rendering quality.
@inproceedings{zhan2025cat3dgs,
author = {Yu-Ting Zhan and Cheng-Yuan Ho and Hebi Yang and Yi-Hsin Chen and Jui Chiu Chiang and Yu-Lun Liu and Wen-Hsiao Peng},
title = {{CAT-3DGS: A context-adaptive triplane approach to rate-distortion-optimized 3DGS compression}},
booktitle = {Proceedings of the Thirteenth International Conference on Learning Representations (ICLR)},
year = {2025},
}