A tensor voting approach for multi-view 3d scene flow estimation and refinement
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
On the evaluation of scene flow estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Consistent Binocular Depth and Scene Flow with Chained Temporal Profiles
International Journal of Computer Vision
Local scene flow by tracking in intensity and depth
Journal of Visual Communication and Image Representation
Estimating scene flow using an interconnected patch surface model with belief-propagation inference
Computer Vision and Image Understanding
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We present an approach to 3D scene flow estimation, which exploits that in realistic scenarios image motion is frequently dominated by observer motion and independent, but rigid object motion. We cast the dense estimation of both scene structure and 3D motion from sequences of two or more views as a single energy minimization problem. We show that agnostic smoothness priors, such as the popular total variation, are biased against motion discontinuities in viewing direction. Instead, we propose to regularize by encouraging local rigidity of the 3D scene. We derive a local rigidity constraint of the 3D scene flow and define a smoothness term that penalizes deviations from that constraint, thus favoring solutions that consist largely of rigidly moving parts. Our experiments show that the new rigid motion prior reduces the 3D flow error by 42% compared to standard TV regularization with the same data term.