3R-GS联合优化相机位姿与3D高斯，提升重建精度与鲁棒性。
3R-GS: Best Practice in Optimizing Camera Poses Along with 3DGS
- 融合MASt3R-SfM先验，联合优化3D高斯与相机参数
- 在纹理缺失等挑战场景下仍保持高质量重建结果
- 适合追求高精度三维重建的科研与工业应用
3D高斯泼溅（3DGS）以高效与高质量重塑了神经渲染，但其性能高度依赖于结构光束法测量（SfM）系统提供的精确相机位姿。尽管近期SfM流程取得显著进展，但在复杂场景（如无纹理区域）中如何同时提升鲁棒性与相机参数估计精度仍是难题。本文提出3R-GS，通过结合大型重建先验MASt3R-SfM，实现3D高斯与相机参数的联合优化。我们指出，直接联合优化面临两大挑战：对SfM初始值敏感，且全局优化能力有限，导致重建效果不理想。3R-GS通过引入优化实践，克服上述问题，即使在相机位姿不准确的情况下也能实现鲁棒重建。大量实验表明，3R-GS在保持计算效率的同时，实现了高质量的新视角合成与精准相机位姿估计。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has revolutionized neural rendering with its efficiency and quality, but like many novel view synthesis methods, it heavily depends on accurate camera poses from Structure-from-Motion (SfM) systems. Although recent SfM pipelines have made impressive progress, questions remain about how to further improve both their robust performance in challenging conditions (e.g., textureless scenes) and the precision of camera parameter estimation simultaneously. We present 3R-GS, a 3D Gaussian Splatting framework that bridges this gap by jointly optimizing 3D Gaussians and camera parameters from large reconstruction priors MASt3R-SfM. We note that naively performing joint 3D Gaussian and camera optimization faces two challenges: the sensitivity to the quality of SfM initialization, and its limited capacity for global optimization, leading to suboptimal reconstruction results. Our 3R-GS, overcomes these issues by incorporating optimized practices, enabling robust scene reconstruction even with imperfect camera registration. Extensive experiments demonstrate that 3R-GS delivers high-quality novel view synthesis and precise camera pose estimation while remaining computationally efficient. Project page: https://zsh523.github.io/3R-GS/
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