鲁棒 Gaussian Splatting
Robust Gaussian Splatting
AI总结：
针对 3DGS 在模糊、位姿误差和颜色不一致下的鲁棒性问题，本文将运动模糊建模为相机位姿的 Gaussian 分布，并加入散焦补偿与颜色校正，在 Scannet++、Deblur-NeRF 上达到最先进性能。
摘要：
本文解决 3D Gaussian Splatting（3DGS）的常见误差来源，包括模糊、不完美的相机位姿和颜色不一致，目标是提升其在实际应用中的鲁棒性，例如从手持手机拍摄内容进行重建。我们的主要贡献是将运动模糊建模为相机位姿上的 Gaussian 分布，从而能够以统一方式同时处理相机位姿精化和运动模糊校正。此外，我们提出了散焦模糊补偿机制，以及处理由环境光、阴影或相机相关因素（如变化的白平衡设置）导致的颜色不一致的机制。所提解决方案能够与 3DGS 公式无缝集成，同时保持其在训练效率和渲染速度方面的优势。我们在 Scannet++ 和 Deblur-NeRF 等相关基准数据集上通过实验验证了这些贡献，取得了最先进结果，并因此相对于相关基线获得一致提升。
英文摘要：
In this paper, we address common error sources for 3D Gaussian Splatting (3DGS) including blur, imperfect camera poses, and color inconsistencies, with the goal of improving its robustness for practical applications like reconstructions from handheld phone captures. Our main contribution involves modeling motion blur as a Gaussian distribution over camera poses, allowing us to address both camera pose refinement and motion blur correction in a unified way. Additionally, we propose mechanisms for defocus blur compensation and for addressing color in-consistencies caused by ambient light, shadows, or due to camera-related factors like varying white balancing settings. Our proposed solutions integrate in a seamless way with the 3DGS formulation while maintaining its benefits in terms of training efficiency and rendering speed. We experimentally validate our contributions on relevant benchmark datasets including Scannet++ and Deblur-NeRF, obtaining state-of-the-art results and thus consistent improvements over relevant baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
