FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent

This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, cam-era intrinsics, and perframe 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° trajectories using Gaussian Splatting. Our method not only far outperforms prior gradient-descent based bundle adjustment methods, but surprisingly approaches the accuracy of COLMAP, the state-of-the-art SfM method, on the downstream task of 360° novel view synthesis-even though our method is purely gradient-descent based, fully differentiable, and presents a complete departure from conventional SfM.