GARO：用于实时高保真动态高斯泼溅的几何感知冗余优化
GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting
- Northwestern Polytechnical University（西北工业大学）
- Université Sorbonne Paris Nord（巴黎第十三大学）
- EmboMind Research（EmboMind研究院）
- Suzhou University of Science and Technology（苏州科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对动态高斯泼溅中冗余导致的内存与渲染效率问题，提出几何感知冗余优化框架，通过低梯度与低曲率筛选剪除冗余点，在保持PSNR稳定的同时将渲染速度提升2倍。
AI中文摘要：
新视角合成是动态场景重建中的关键任务，其中高渲染速度对于虚拟现实等应用至关重要。现有的可变形高斯泼溅方法实现了高保真的动态场景建模，但由于大量冗余高斯的存在，在内存使用和渲染效率方面仍面临限制。为解决这些挑战，我们提出了几何感知冗余优化（GARO），一种在传统动态场景重建流程的自适应密度控制阶段中的统一冗余度量框架。该框架首先利用优化活动评估策略选择低梯度候选点，然后通过低曲率分析评估几何复杂度，以进一步过滤和剪除冗余点，从而得到紧凑且具有表达力的高斯表示。在合成和真实世界数据集上的大量实验表明，GARO在质量和速度之间实现了稳健的权衡，PSNR保持稳定，渲染速度提升了2倍，验证了GARO的效率和有效性。
英文摘要：
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.
