Relaxed Rigidity with Ray-based Grouping for Dynamic Gaussian Splatting

TL;DR

This paper introduces a novel view-space ray grouping strategy to preserve local geometric structure in dynamic 3D scene reconstruction, improving temporal coherence without external priors.

Contribution

The method explicitly maintains Gaussian local geometry over time using ray-based clustering, enhancing physical plausibility and reconstruction quality in monocular videos.

Findings

Outperforms existing methods on monocular datasets

Achieves superior temporal consistency

Enhances local geometric stability over time

Abstract

The reconstruction of dynamic 3D scenes using 3D Gaussian Splatting has shown significant promise. A key challenge, however, remains in modeling realistic motion, as most methods fail to align the motion of Gaussians with real-world physical dynamics. This misalignment is particularly problematic for monocular video datasets, where failing to maintain coherent motion undermines local geometric structure, ultimately leading to degraded reconstruction quality. Consequently, many state-of-the-art approaches rely heavily on external priors, such as optical flow or 2D tracks, to enforce temporal coherence. In this work, we propose a novel method to explicitly preserve the local geometric structure of Gaussians across time in 4D scenes. Our core idea is to introduce a view-space ray grouping strategy that clusters Gaussians intersected by the same ray, considering only those whose $\alpha$-blending weights exceed a threshold. We then apply constraints to these groups to maintain a consistent spatial distribution, effectively preserving their local geometry. This approach enforces a more physically plausible motion model by ensuring that local geometry remains stable over time, eliminating the reliance on external guidance. We demonstrate the efficacy of our method by integrating it into two distinct baseline models. Extensive experiments on challenging monocular datasets show that our approach significantly outperforms existing methods, achieving superior temporal consistency and reconstruction quality.