Temporal Smoothness-Aware Rate-Distortion Optimized 4D Gaussian Splatting

TL;DR

This paper presents a novel end-to-end rate-distortion optimized compression framework for 4D Gaussian Splatting, significantly reducing storage requirements while maintaining high visual fidelity for dynamic scene rendering.

Contribution

It introduces a wavelet-based temporal smoothness prior and an RD-optimized approach for compressing 4D Gaussian Splatting, enabling efficient, high-quality rendering across platforms.

Findings

Achieves up to 91× compression ratio.

Maintains high visual fidelity in dynamic scene rendering.

Enables real-time rendering on resource-constrained devices.

Abstract

Dynamic 4D Gaussian Splatting (4DGS) effectively extends the high-speed rendering capabilities of 3D Gaussian Splatting (3DGS) to represent volumetric videos. However, the large number of Gaussians, substantial temporal redundancies, and especially the absence of an entropy-aware compression framework result in large storage requirements. Consequently, this poses significant challenges for practical deployment, efficient edge-device processing, and data transmission. In this paper, we introduce a novel end-to-end RD-optimized compression framework tailored for 4DGS, aiming to enable flexible, high-fidelity rendering across varied computational platforms. Leveraging Fully Explicit Dynamic Gaussian Splatting (Ex4DGS), one of the state-of-the-art 4DGS methods, as our baseline, we start from the existing 3DGS compression methods for compatibility while effectively addressing additional challenges introduced by the temporal axis. In particular, instead of storing motion trajectories independently per point, we employ a wavelet transform to reflect the real-world smoothness prior, significantly enhancing storage efficiency. This approach yields significantly improved compression ratios and provides a user-controlled balance between compression efficiency and rendering quality. Extensive experiments demonstrate the effectiveness of our method, achieving up to 91$\times$ compression compared to the original Ex4DGS model while maintaining high visual fidelity. These results highlight the applicability of our framework for real-time dynamic scene rendering in diverse scenarios, from resource-constrained edge devices to high-performance environments. The source code is available at https://github.com/HyeongminLEE/RD4DGS.