Optical Flow in Mostly Rigid Scenes
This addresses the challenge of accurate optical flow estimation in dynamic scenes for computer vision applications, representing an incremental improvement by integrating existing approaches.
The paper tackles the problem of optical flow in scenes with both static and moving objects by combining camera motion estimation with explicit segmentation of moving regions, achieving state-of-the-art results on MPI-Sintel and KITTI-2015 benchmarks.
The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPI-Sintel and KITTI-2015 benchmarks.