Back to Search Start Over

Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes

Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes

Authors :
Javed, Saqib
Khan, Ahmad Jarrar
Dumery, Corentin
Zhao, Chen
Salzmann, Mathieu
Publication Year :
2024

Abstract

Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory usage and improvements in rendering efficiency are required. While many state-of-the-art methods prioritize lightweight implementations, they struggle in handling scenes with complex motions or long sequences. In this work, we introduce Temporally Compressed 3D Gaussian Splatting (TC3DGS), a novel technique designed specifically to effectively compress dynamic 3D Gaussian representations. TC3DGS selectively prunes Gaussians based on their temporal relevance and employs gradient-aware mixed-precision quantization to dynamically compress Gaussian parameters. It additionally relies on a variation of the Ramer-Douglas-Peucker algorithm in a post-processing step to further reduce storage by interpolating Gaussian trajectories across frames. Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$\times$ compression with minimal or no degradation in visual quality.<br />Comment: Code will be released soon

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2412.05700
Document Type :
Working Paper