11,762 research outputs found
Optical Flow in Mostly Rigid Scenes
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.Comment: 15 pages, 10 figures; accepted for publication at CVPR 201
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
Unsupervised deep learning for optical flow computation has achieved
promising results. Most existing deep-net based methods rely on image
brightness consistency and local smoothness constraint to train the networks.
Their performance degrades at regions where repetitive textures or occlusions
occur. In this paper, we propose Deep Epipolar Flow, an unsupervised optical
flow method which incorporates global geometric constraints into network
learning. In particular, we investigate multiple ways of enforcing the epipolar
constraint in flow estimation. To alleviate a "chicken-and-egg" type of problem
encountered in dynamic scenes where multiple motions may be present, we propose
a low-rank constraint as well as a union-of-subspaces constraint for training.
Experimental results on various benchmarking datasets show that our method
achieves competitive performance compared with supervised methods and
outperforms state-of-the-art unsupervised deep-learning methods.Comment: CVPR 201
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
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