FlowMap：基于梯度下降的高质量相机位姿、内参和深度估计
FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent
AI总结：
本文提出FlowMap，一种端到端可微方法，通过梯度下降最小化光流目标函数，联合估计视频序列的相机位姿、内参和密集深度，在360度新视角合成任务中表现媲美COLMAP。
摘要：
本文提出了FlowMap，这是一种端到端可微方法，用于求解视频序列的精确相机位姿、相机内参和逐帧密集深度。该方法通过对每个视频执行梯度下降，最小化一个简单的最小二乘目标函数，将深度、内参和位姿诱导的光流与通过现成光流和点追踪获得的对应关系进行比较。除了使用点轨迹来促进长期几何一致性外，我们还引入了深度、内参和位姿的可微重参数化，使其适用于一阶优化。我们通过实验证明，该方法恢复的相机参数和密集深度能够使用Gaussian Splatting在360度轨迹上实现照片级真实感的新视角合成。该方法不仅远超以往基于梯度下降的捆绑调整方法，而且在360度新视角合成的下游任务中，其表现竟与最先进的SfM方法COLMAP不相上下（尽管该方法纯粹基于梯度下降，完全可微，并与传统SfM彻底不同）。
英文摘要：
This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence. Our method performs per-video gradient-descent minimization of a simple least-squares objective that compares the optical flow induced by depth, intrinsics, and poses against correspondences obtained via off-the-shelf optical flow and point tracking. Alongside the use of point tracks to encourage long-term geometric consistency, we introduce differentiable re-parameterizations of depth, intrinsics, and pose that are amenable to first-order optimization. We empirically show that camera parameters and dense depth recovered by our method enable photo-realistic novel view synthesis on 360-degree trajectories using Gaussian Splatting. Our method not only far outperforms prior gradient-descent based bundle adjustment methods, but surprisingly performs on par with COLMAP, the state-of-the-art SfM method, on the downstream task of 360-degree novel view synthesis (even though our method is purely gradient-descent based, fully differentiable, and presents a complete departure from conventional SfM).
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。
