Dense Semi-rigid Scene Flow Estimation from RGBD Images

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Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “In contrast to optical flow which provides the projection of the scene motion onto the image plane, scene flow [41, 6, 26, 8, 11, 37, 32] estimates the actual 3D motion field.”
SE3-nets: Learning rigid body motion using deep neural networks (2016, neutral): “We also vary the table size to introduce background variations.”
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation (2018, methodology): “Scene ﬂow estimation was popularized for the ﬁrst time by the work of Vedula et al. [45] and was later dominated by variational methods [15] [33] [47].”
FlowNet3D: Learning Scene Flow in 3D Point Clouds (2018, unknown): “With the recent advent of commodity depth sensors, it has become feasible to estimate scene flow from monocular RGB-D images [9], by generalizing variational 2D flow algorithms to 3D [10, 14] and exploiting more geometric cues provided by the depth channel [21, 11, 23].”
Layered RGBD scene flow estimation (2015, methodology): “Teddy Cones RMS AAE RMS AAE Proposed (K = 4) 0.09 0.17 0.12 0.13 SRSF [27] 0.49 0.46 0.45 0.37 Hadfield and Bowden [17] 0.52 1.36 0.59 1.61 Basha et al. [7] (2 views) 0.57 1.01 0.58 0.39 Quiroga et al. [28] 0.94 0.84 0.79 0.52 FC-2Layers-FF [41]∗ 1.90 0.24 2.80 0.18 Classic+NLP [38]∗ 2.00 0.24 2.79 ”