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.

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 An Adaptive Smoothness Parameter Strategy for Variational Optical Flow Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hussain Zaid H. Alsharif ◽  
Tong Shu ◽  
Bin Zhu ◽  
Zeyad Farisi
Keyword(s):  
Image Quality ◽  
Optical Flow ◽  
Flow Model ◽  
Quality Parameters ◽  
Flow Models ◽  
Flow Estimation ◽  
Proposed Model ◽  
Smoothness Parameter ◽  
Smoothness Term ◽  
Public Datasets

The smoothness parameter is used to balance the weight of the data term and the smoothness term in variational optical flow model, which plays very significant role for the optical flow estimation, but existing methods fail to obtain the optimal smoothness parameters (OSP). In order to solve this problem, an adaptive smoothness parameter strategy is proposed. First, an amalgamated simple linear iterative cluster (SLIC) and local membership function (LMF) algorithm is used to segment the entire image into several superpixel regions. Then, image quality parameters (IQP) are calculated, respectively, for each superpixel region. Finally, a neural network model is applied to compute the smoothness parameter by these image quality parameters of each superpixel region. Experiments were done in three public datasets (Middlebury, MPI_Sintel, and KITTI) and our self-constructed outdoor dataset with the proposed method and other existing classical methods; the results show that our OSP method achieves higher accuracy than other smoothness parameter selection methods in all these four datasets. Combined with the dual fractional order variational optical flow model (DFOVOFM), the proposed model shows better performance than other models in scenes with illumination inhomogeneity and abnormity. The OSP method fills the blank of the research of adaptive smoothness parameter, pushing the development of the variational optical flow models.

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.

Directionally Weakened Diffusion for Optical Flow

2011 ◽  
Vol 403-408 ◽  
pp. 2449-2452
Author(s):  
Jie Zhao ◽  
Huai Bin Wang ◽  
Yuan Quan Wang
Keyword(s):  
Optical Flow ◽  
New Method ◽  
Accurate Estimation ◽  
Laplacian Operator ◽  
Flow Estimation ◽  
Optical Flow Computation ◽  
Computer Vision Applications ◽  
Smoothness Term ◽  
Motion Discontinuities ◽  
Data Term

Optical flow estimation from image sequences is of paramount importance to computer vision applications. Many optical flow algorithms have been proposed for optical flow computation, which minimize a certain energy function involving a data term and a smoothness term. In this paper, we propose a new method to compute optical flow by decomposing the Laplacian operator along the tangential and gradient direction of the image. Experimental results show that the new method could gain more accurate estimation of optical flow around motion discontinuities compared with the classical methods.

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
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