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arXiv · 2505.22908

Learning Hierarchical Sparse Transform Coding for 3DGS Compression

Hao Xu·Xiaolin Wu·Xi Zhang
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Abstract

Current 3DGS compression methods largely forego the neural analysis-synthesis transform, which is a crucial component in learned signal compression systems. As a result, redundancy removal is left solely to the entropy coder, overburdening the entropy coding module and reducing rate-distortion (R-D) performance. To fix this critical omission, we propose a training-time transform coding (TTC) method that adds the analysis-synthesis transform and optimizes it jointly with the 3DGS representation and entropy model. Concretely, we adopt a hierarchical design: a channel-wise KLT for decorrelation and energy compaction, followed by a sparsity-aware neural transform that reconstructs the KLT residuals with minimal parameter and computational overhead. Experiments show that our method delivers strong R-D performance with fast decoding, offering a favorable BD-rate-decoding-time trade-off over SOTA 3DGS compressors.

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Hao Xu, Xiaolin Wu, Xi Zhang. 2025-05-28. Learning Hierarchical Sparse Transform Coding for 3DGS Compression. https://arxiv.org/abs/2505.22908

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