scholarly journals A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

2016 ◽  
Author(s):  
Nikolaus Mayer ◽  
Eddy Ilg ◽  
Philip Hausser ◽  
Philipp Fischer ◽  
Daniel Cremers ◽  
...  
Keyword(s):  
Optical Flow ◽  
Large Dataset ◽  
Flow Estimation ◽  
Convolutional Networks ◽  
Scene Flow

Optical flow and scene flow estimation: A survey

2021 ◽  
Vol 114 ◽  
pp. 107861
Author(s):  
Mingliang Zhai ◽  
Xuezhi Xiang ◽  
Ning Lv ◽  
Xiangdong Kong
Keyword(s):  
Optical Flow ◽  
Flow Estimation ◽  
Scene Flow

scholarly journals Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity

2019 ◽  
Author(s):  
Liang Liu ◽  
Guangyao Zhai ◽  
Wenlong Ye ◽  
Yong Liu
Keyword(s):  
Optical Flow ◽  
Unsupervised Learning ◽  
Optical Networks ◽  
Object Motion ◽  
Camera Motion ◽  
Outlier Rejection ◽  
Unified Framework ◽  
Flow Estimation ◽  
Local Rigidity ◽  
Scene Flow

Scene flow estimation in the dynamic scene remains a challenging task. Computing scene flow by a combination of 2D optical flow and depth has shown to be considerably faster with acceptable performance. In this work, we present a unified framework for joint unsupervised learning of stereo depth and optical flow with explicit local rigidity to estimate scene flow. We estimate camera motion directly by a Perspective-n-Point method from the optical flow and depth predictions, with RANSAC outlier rejection scheme. In order to disambiguate the object motion and the camera motion in the scene, we distinguish the rigid region by the re-project error and the photometric similarity. By joint learning with the local rigidity, both depth and optical networks can be refined. This framework boosts all four tasks: depth, optical flow, camera motion estimation, and object motion segmentation. Through the evaluation on the KITTI benchmark, we show that the proposed framework achieves state-of-the-art results amongst unsupervised methods. Our models and code are available at https://github.com/lliuz/unrigidflow.

scholarly journals Automatic layered RGB-D scene flow estimation with optical flow field constraint

2020 ◽  
Author(s):  
Xiuxiu Li ◽  
Yanjuan Liu ◽  
Haiyan Jin ◽  
Jiangbin Zheng ◽  
Lei Cai
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Flow Estimation ◽  
Optical Flow Field ◽  
Scene Flow

scholarly journals Scene Flow Estimation Based on Adaptive Anisotropic Total Variation Flow-Driven Method

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xuezhi Xiang ◽  
Rongfang Zhang ◽  
Mingliang Zhai ◽  
Deguang Xiao ◽  
Erwei Bai
Keyword(s):  
Optical Flow ◽  
Total Variation ◽  
Stereo Image ◽  
Anisotropic Flow ◽  
Flow Estimation ◽  
Real Scene ◽  
Total Variation Flow ◽  
Scene Flow ◽  
Smoothness Term ◽  
Anisotropic Total Variation

Scene flow estimation based on disparity and optical flow is a challenging task. We present a novel method based on adaptive anisotropic total variation flow-driven method for scene flow estimation from a calibrated stereo image sequence. The basic idea is that diffusion of flow field in different directions has different rates, which can be used to calculate total variation and anisotropic diffusion automatically. Brightness consistency and gradient consistency constraint are employed to establish the data term, and adaptive anisotropic flow-driven penalty constraint is employed to establish the smoothness term. Similar to the optical flow estimation, there are also large displacement problems in the estimation of the scene flow, which is solved by introducing a hierarchical computing optimization. The proposed method is verified by using the synthetic dataset and the real scene image sequences. The experimental results show the effectiveness of the proposed algorithm.

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