GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting
Huiwen Xue, Kaixing Zhao, Zuheng MING, Tingcheng Li
AI summary
Problem
Dynamic 3D Gaussian Splatting methods accumulate redundant Gaussians during training, leading to excessive memory usage and slow rendering speeds that hinder real-time applications.
Approach
The method introduces a unified redundancy measurement framework that identifies and removes redundant Gaussians by jointly evaluating their gradient-based optimization activity and local geometric curvature during adaptive density control.
Key results
- Over 2× rendering speedup on synthetic and real-world datasets
- Significant reduction in Gaussian count and GPU memory consumption
- Maintains comparable or superior PSNR and SSIM reconstruction quality
- Effectively prunes flat-region Gaussians while preserving high-curvature details
Why it matters
Enables efficient, real-time dynamic scene reconstruction for VR, robotics, and autonomous driving applications with limited computational resources.
Abstract
Novel view synthesis is a key task for dynamic scene reconstruction, where high rendering speed is essential for applications such as virtual reality. Existing deformable Gaussian Splatting methods achieve high-fidelity dynamic scene modeling, but still face limitations in memory usage and rendering efficiency due to the large number of redundant Gaussians. To address these challenges, we propose Geometry- Aware Redundancy Optimization (GARO), a unified redun- dancy measurement framework in the adaptive density control stage of the traditional dynamic scene reconstruction pipeline. This framework first selects low-gradient candidates using an optimization activity assessment strategy, and then evaluates geometric complexity through low curvature analysis to further filter and prune redundant points, resulting in a compact and expressive Gaussian representation. Extensive experiments on synthetic and real-world datasets demonstrate that GARO achieves robust trade-offs between quality and speed, with PSNR remaining stable and rendering speed improved by 2×, validating the efficiency and effectiveness of GARO.