3DGS Compression with Sparsity-guided Hierarchical Transform Coding

Hao Xu, Xiaolin Wu, Xi Zhang

Abstract: 3D Gaussian Splatting (3DGS) supports fast, high quality, novel view synthesis but has a heavy memory footprint, making the compression of its model crucial. Current state-of-the-art (SOTA) 3DGS compression methods adopt an anchor-based architecture that pairs the Scaffold-GS representation with conditional entropy coding. However, these methods forego the analysis-synthesis transform, a vital mechanism in visual data compression. As a result, redundancy remains intact in the signal and its removal is left to the entropy coder, which computationally overburdens the entropy coding module, increasing coding latency. Even with added complexity thorough redundancy removal is a task unsuited to an entropy coder. To fix this critical omission, we introduce a Sparsity-guided Hierarchical Transform Coding (SHTC) method, the first study on the end-to-end learned neural transform coding of 3DGS. SHTC applies KLT to decorrelate intra-anchor attributes, followed by quantization and entropy coding, and then compresses KLT residuals with a low-complexity, scene-adaptive neural transform. Aided by the sparsity prior and deep unfolding technique, the learned transform uses only a few trainable parameters, reducing the memory usage. Overall, SHTC achieves an appreciably improved R-D performance and at the same time higher decoding speed over SOTA. Its prior-guided, parameter-efficient design may also inspire low-complexity neural image and video codecs. Our code will be released at https://github.com/hxu160/SHTC_for_3DGS_compression.

External IDs:dblp:journals/corr/abs-2505-22908