Abstract
Scene flow estimation based on disparity and optical flow is a challenging task. We present a novel method based on adaptive anisotropic total variation flow-driven method for scene flow estimation from a calibrated stereo image sequence. The basic idea is that diffusion of flow field in different directions has different rates, which can be used to calculate total variation and anisotropic diffusion automatically. Brightness consistency and gradient consistency constraint are employed to establish the data term, and adaptive anisotropic flow-driven penalty constraint is employed to establish the smoothness term. Similar to the optical flow estimation, there are also large displacement problems in the estimation of the scene flow, which is solved by introducing a hierarchical computing optimization. The proposed method is verified by using the synthetic dataset and the real scene image sequences. The experimental results show the effectiveness of the proposed algorithm.
Highlights
Scene flow was first introduced by Vedula et al [1] in 1999
We present a novel method based on adaptive anisotropic total variation flow-driven method for scene flow estimation from a calibrated stereo image sequence
Inspired by optical flow regularizers, we propose a scene flow estimation method, which can take the advantages of anisotropic total variation and adaptive regularizer
Summary
Scene flow was first introduced by Vedula et al [1] in 1999 It is defined as a dense 3D motion field that describes the change of structured light in the image surfaces, which can be estimated from a calibrated stereo image sequence. One is variational minimization method of scene flow based on the optical flow and the disparity from stereo image sequence [5]. The other is 3D point cloud parametrization method based on the 3D structure to estimate the scene flow, which allows us to directly estimate the desired unknowns [6]. Under these two frameworks, a lot of researches on optical flow estimation can be used for scene flow estimation
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