FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent
<|reference_start|>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, 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).<|reference_end|>
@article{smith2024flowmap:,
    title={FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient
  Descent},
    author={Cameron Smith and David Charatan and Ayush Tewari and Vincent Sitzmann},
    journal={arXiv preprint arXiv:2404.15259},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15259},
    primaryClass={cs.CV}
}