3D Scene Flow Estimation with a Piecewise Rigid Scene Model

By Christoph Vogel, Konrad Schindler, Stefan Roth. 2015

244 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (2015, methodology): “[24] combined the task of scene flow estimation with superpixel segmentation using a piecewise rigid model for regularization.”
Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning (2018, unknown): “[63] 6.”
FlowNet3D: Learning Scene Flow in 3D Point Clouds (2018, methodology): “PRSM [30] uses energy minimization on rigidly moving segments and jointly estimates multiple attributes together including rigid motion.”
DSNet: Joint Learning for Scene Segmentation and Disparity Estimation (2019, unknown): “…77.3 TuSimple [34] 77.6 SAC-multiple [42] 78.1 PSPNet [43] 78.4 DeepLabv3+ [5] 79.6 3.43 PBCP [30] 3.61 MC-CNN-acrt [39] 3.89 chen17 [7] 4.07 PRSM [33] 4.27 DispNetC [26] 4.34 Content-CNN [25] 4.54 SPS-St [37] 5.31 OSF [27] 5.79 MDP [20] 6.74 robust features, which widely used in [28] [31] [38].”
Learning Motion Patterns in Videos (2016, methodology): “, 3D motion field in a scene [37], is another form of motion estimation, but is computed with additional information, such as disparity values computed from stereo images [15, 40], or estimated 3D scene models [38].”
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose (2018, background): “Top ranked meth-ods on the KITTI benchmark typically involve the joint reasoning of geometry, rigid motion and segmentation [3, 51].”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “When the goal is to represent real-world scenes with independent object motion as in scene flow or optical flow, superpixels can be generalized to rigidly moving segments (Vogel et al. (2015); Menze & Geiger (2015)), or semantic segments (Bai et al. (2016); Sevilla-Lara et al. (2016)).”