2013 IEEE International Conference on Computer Vision 2013
DOI: 10.1109/iccv.2013.174
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Cited by 184 publications
(184 citation statements)
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“…Only recently, scene flow algorithms have begun to outperform the former two [16]. This gives rise to the idea of multimedia content production tools based on scene flow.…”
Section: Mm'15 October 26 -30 2015 Brisbane Australiamentioning
confidence: 99%
“…Only recently, scene flow algorithms have begun to outperform the former two [16]. This gives rise to the idea of multimedia content production tools based on scene flow.…”
Section: Mm'15 October 26 -30 2015 Brisbane Australiamentioning
confidence: 99%
“…Three-dimensional (3D) tracking of objects became feasible using scene flow in images. Scene flow combines stereo matching and optical flow to estimate the 3D motion of the pixels in a scene [5][6][7]. However, scene flow is still not reliable for critical applications, and researchers continue working on improving the results of scene flow algorithms.…”
Section: Literature Reviewmentioning
confidence: 99%
“…As in optical flow estimation, this approach eventually fails to recover large displacements of small objects. Following recent developments in optical flow (Yamaguchi et al, 2013, Nir et al, 2008, Wulff and Black, 2014, Sun et al, 2013 and stereo (Yamaguchi et al, 2014, Bleyer et al, 2011, Bleyer et al, 2012, Vogel et al (Vogel et al, 2013, Vogel et al, 2014 proposed a slanted-plane model which assigns each pixel to an image segment and each segment to one of several rigidly moving 3D plane proposals, thus casting the task as a discrete optimization problem. Fusion moves are leveraged for solving binary subproblems with quadratic pseudo-boolean optimization (QPBO) (Rother et al, 2007).…”
Section: Related Workmentioning
confidence: 99%
“…Furthermore, depth information allows for a more meaningful parametrization of the problem in 3D object space. Recent algorithms for scene flow estimation leverage this fact (Vogel et al, 2013, Vogel et al, 2014 and provide promising segmentations of the images into individually moving objects (Menze and Geiger, 2015).…”
Section: Introductionmentioning
confidence: 99%