Robust, Estimator-Agnostic Dynamic 3DGS Compression

2026-09-21

AI Summary

该研究提出一种鲁棒的3D高斯点云压缩方法，通过将多帧合并为单个高斯集合并利用静态编码器处理，以减少对估计器的依赖，有效降低了比特率。

Abstract

Dynamic 3D Gaussian splats (3DGS) model time-varying scenes using a separate Gaussian set per frame. While neighboring video frames are highly correlated due to smooth motion, Gaussian representations retain this correlation to varying degrees, depending on whether the estimator tracks them across time. Some 3DGS compression methods integrate the estimation to exploit temporal redundancy; here, we focus on robust compression regardless of the estimator. We concatenate groups of frames into one Gaussian set, augment each Gaussian with a frame index, and pass it to a static (i.e., non-temporal) 3DGS codec, converting temporal redundancy into spatial redundancy. Concatenated sets are spatially partitioned to limit memory. Our technique requires neither a motion model nor knowledge of the training method. Averaged over six N3DV sequences, all six static codecs achieve gains on tracked sets (-42.0% to -71.8% BD-rate) over per-frame coding. On untracked sets, all codecs except HGSC, which appears incompatible with our technique, remain competitive with per-frame coding (-3.5% to +5.0%). We further replace D-FCGS's I-frame coding with our technique while retaining its P-frame coding, yielding an overall BD-rate of -46.2%. We propose to visualize "trackedness" using an inter-frame similarity metric. The project is available at https://wcjj1236.github.io/d3dgs-benchmark.

Problem

• Dynamic 3DGS
• compression
• temporal redundancy
• estimator-agnostic
• robust

Innovation

• Robust Compression
• Estimator-Agnostic
• Temporal Redundancy to Spatial Redundancy
• 3D Gaussian Splats (3DGS)
• Bitrate Reduction