Multi-view Scene Flow Estimation: A View Centered Variational Approach

By Tali Basha, Yael Moses, Nahum Kiryati. 2010

201 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “These methods either extracted features from one modality: depth [65, 78, 42, 77, 37, 67, 68] or RGB [6, 40, 46] or skeleton [76, 75, 22, 13, 57, 72, 85, 8, 47], or fuse the features extracted separately from them at a later stage [ In contrast, we propose to adopt scene ﬂow for 3D action…”
A primal-dual framework for real-time dense RGB-D scene flow (2015, methodology): “To allow for discontinuities in the motion field, Total Variation (TV) was adopted in [6] and [7], where they each present a unified variational formulation to compute the disparity and the motion field jointly.”
3D Scene Flow Estimation with a Piecewise Rigid Scene Model (2015, methodology): “15 we extend our standard 2D proposal set by adding proposals from 3D scene ﬂow methods ( 3D-Props ), namely L 1 -regularized 3D scene ﬂow (Basha et al. 2010) and locally rigid 3D scene ﬂow (Vogel et al. 2011).”
Optical flow modeling and computation: A survey (2015, background): “The problem of recovering the 3D motion itself from several synchronized cameras is also an active research domain known as scene ﬂow estimation [18,248].”
SphereFlow: 6 DoF Scene Flow from RGB-D Pairs (2014, background): “[4]—who use only RGB images as input—in end point error (RMS-OF) and average angular error (AAE) over the 2D optical flow vectors obtained by projecting the output 3D displacements to image space.”
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation (2014, background): “Several papers have tackled the problem of estimating the 3D flow-field [28,17,14,2].”
Object scene flow for autonomous vehicles (2015, methodology): “Therefore, synthetic renderings of spheres [18, 32], primitives [1, 6, 36] or simple street scenes [25, 38, 39] are typically employed to measure quantitative performance.”
Kinecting the dots: Particle based scene flow from depth sensors (2011, methodology): “As in [1], parameters are estimated for the camera setup.”
Total Moving Face Reconstruction (2014, background): “, same expression) [37,38] or rigid motion throughout the video [4].”
Dense Semi-rigid Scene Flow Estimation from RGBD Images (2014, methodology): “The Middlebury stereo dataset [15] is commonly used as benchmark to compare scene flow methods [1,8,5,13].”
Scene flow by tracking in intensity and depth data (2012, methodology): “Table 4 presents a summary of the errors of methods proposed in [16], [4] and [13] in comparison with the proposed algorithm in this work.”
Joint 3d Estimation of Vehicles and Scene Flow (2015, unknown): “Like in classical optical ﬂow estimation (Horn and Schunck, 1980), the problem is often formulated in a coarse-to-ﬁne variational setting (Basha et al., 2013, Huguet and Devernay, 2007, Pons et al., 2007, Valgaerts et al., 2010, Wedel et al., 2011, Vogel et al., 2011) and local regularizers are…”
Learning the Depths of Moving People by Watching Frozen People (2019, methodology): “Depth information captured from RGBD sensors or stereo cameras has been widely used for 3D modeling of dynamic scenes [1], [2], [7], [18], [20], [32], [38], [51], [59], [66].”
FlowNet3D: Learning Scene Flow in 3D Point Clouds (2018, background): “Since this seminal work, many others have tried to jointly estimate structure and motion from stereoscopic images [12, 18, 34, 26, 5, 33, 28, 29, 1, 30, 16], mostly in a variational setting with regularizations for smoothness of motion and structure [12, 1, 26], or with assumption of the rigidity of”