Piecewise Rigid Scene Flow

By Christoph Vogel, Konrad Schindler, Stefan Roth. 2013

244 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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In Media Technology

Citations in the corpus

Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “Since this work, several methods [64, 57, 44] have been proposed based on stereo or multiple view camera systems.”
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (2015, methodology): “[25] combined the task of scene flow estimation with superpixel segmentation using a piecewise rigid model for regularization.”
A multi-block-matching approach for stereo (2015, methodology): “It shows the top two ranking methods at that time: CVPR 1186 [23] and MC-CNN [23] both being anonymous at that time; plus some selected methods: Particle Convex Belief Propagation PCBP [24], SceneFlow [25], SGM with road scene adaption rdSGM [23], Weighted SGM wSGM [26] and summed normalized cross-c”
A primal-dual framework for real-time dense RGB-D scene flow (2015, background): “Moreover, some of the most recent works with stereo cameras [9], [10] propose to exploit the fact that in most realistic scenarios the image motion is composed of several rigid body motions, and 2015 IEEE International Conference on Robotics and Automation (ICRA) Washington State Convention Center S”
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”
Computing the stereo matching cost with a convolutional neural network (2014, unknown): “[17] 4.”
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation (2014, methodology): “Unfortunately, these slanted plane methods have involved time-consuming optimization algorithms (several minutes per frame) such as particle belief propagation [30,31] or algorithms based on plane proposals with fusion moves and iterated cut-based segmentations [26].”
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation (2018, methodology): “[46] combined the task of scene flow estimation with superpixel segmentation using a piecewise rigid model for regularization.”
Object scene flow for autonomous vehicles (2015, methodology): “Finally, we also include the results of Vogel’s piece-wise rigid scene flow (PRSF) approach [37].”
SphereFlow: 6 DoF Scene Flow from RGB-D Pairs (2014, background): “[28] assign rigidly moving planes to image segments.”
Joint 3d Estimation of Vehicles and Scene Flow (2015, methodology): “…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…”
Layered RGBD scene flow estimation (2015, background): “[43] represent a scene with a collection of locally planar, rigidly moving planes and obtain top-ranking results on the KITTI benchmark [13].”
Displets: Resolving stereo ambiguities using object knowledge (2015, unknown): “0 px 10 PR-Sf+E * [58] 4.”
3D Scene Flow Estimation with a Piecewise Rigid Scene Model (2015, methodology): “The present paper is based on two conference publications (Vogel et al. 2013b, 2014).”