GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting

2026-09-20

AI Summary

为解决动态场景重建中内存使用和渲染效率问题，提出几何感知冗余优化（GARO）方法，通过评估和剔除冗余高斯点以提高渲染速度。

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 redundancy 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 2x, validating the efficiency and effectiveness of GARO.

Problem

• Dynamic Scene Reconstruction
• Gaussian Splatting
• Redundancy Optimization
• Rendering Efficiency

Innovation

• Geometry-Aware Redundancy Optimization
• adaptive density control
• low-curvature analysis
• dynamic scene reconstruction