2020
DOI: 10.1007/978-3-030-58604-1_32
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FLOT: Scene Flow on Point Clouds Guided by Optimal Transport

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Cited by 115 publications
(181 citation statements)
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“…Mittal et al [44] and PointPWCNet [75] proposed self-supervised losses to infer the scene flow in an end-to-end manner. Finally, FLOT [51] proposed a simple correspondence-based end-to-end scene flow network. While our backbone also estimates correspondences, decomposing the scene into rigid agents provides us further higher-level scene understanding and enables test-time optimization while requiring less supervision.…”
Section: Related Workmentioning
confidence: 99%
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“…Mittal et al [44] and PointPWCNet [75] proposed self-supervised losses to infer the scene flow in an end-to-end manner. Finally, FLOT [51] proposed a simple correspondence-based end-to-end scene flow network. While our backbone also estimates correspondences, decomposing the scene into rigid agents provides us further higher-level scene understanding and enables test-time optimization while requiring less supervision.…”
Section: Related Workmentioning
confidence: 99%
“…In particular, applications such as self-driving and robot navigation rely upon a robust perception of dynamically changing 3D scenes. To equip autonomous agents with the ability to infer spatiotemporal geometric properties, there has recently been an increased interest in 3D scene flow as a form of lowlevel dynamic scene representation [37,67,73,51,49,54]. Scene flow is the 3D motion field of points in the scene [69] and is a generalization of 2D optical flow.…”
Section: Introductionmentioning
confidence: 99%
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“…3D scene flow can be applied to object detection and tracking (Behl et al, 2017;Lenz et al, 2011;Zhai et al, 2020), LiDAR odometry (Wang et al, 2021e), action recognition (Wang et al, 2017), etc. Recently, some works (Liu et al, 2019a;Wang et al, 2021d;Puy et al, 2020;Li et al, 2021b,a;Wang et al, 2021a) have been done to realize supervised estimation of 3D scene flow from two consecutive frames of point clouds. However, just like it is difficult to obtain the ground truth of optical flow (Wang et al, 2021c(Wang et al, , 2020b, the ground truth of 3D scene flow is also difficult to obtain.…”
Section: Introductionmentioning
confidence: 99%