scholarly journals Stereo Matching Algorithm Based on 2D Delaunay Triangulation

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Xue-he Zhang ◽  
Ge Li ◽  
Chang-le Li ◽  
He Zhang ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Stereo Matching ◽  
Robot Vision ◽  
Depth Estimation ◽  
Planning System ◽  
Disparity Map ◽  
Matching Method ◽  
Running Speed ◽  
Edge Operator ◽  
Edge Points

To fulfill the applications on robot vision, the commonly used stereo matching method for depth estimation is supposed to be efficient in terms of running speed and disparity accuracy. Based on this requirement, Delaunay-based stereo matching method is proposed to achieve the aforementioned standards in this paper. First, a Canny edge operator is used to detect the edge points of an image as supporting points. Those points are then processed using a Delaunay triangulation algorithm to divide the whole image into a series of linked triangular facets. A proposed module composed of these facets performs a rude estimation of image disparity. According to the triangular property of shared vertices, the estimated disparity is then refined to generate the disparity map. The method is tested on Middlebury stereo pairs. The running time of the proposed method is about 1 s and the matching accuracy is 93%. Experimental results show that the proposed method improves both running speed and disparity accuracy, which forms a steady foundation and good application prospect for a robot’s path planning system with stereo camera devices.

scholarly journals Disparity estimation using Graph cuts for road applications

2021 ◽  
Vol 297 ◽  
pp. 01055
Author(s):  
Mohamed El Ansari ◽  
Ilyas El Jaafari ◽  
Lahcen Koutti
Keyword(s):  
Stereo Matching ◽  
Optimal Solution ◽  
Similarity Criterion ◽  
Graph Cuts ◽  
Search Space ◽  
Disparity Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Current Frame ◽  
Edge Points

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.

Image Stereo Matching Based on Multi-Scale Plane Set

2013 ◽  
Vol 709 ◽  
pp. 527-533 ◽  
Author(s):  
Xin Hui Jiang ◽  
Shao Jun Yu ◽  
Xing Jiang
Keyword(s):  
Dynamic Programming ◽  
Stereo Matching ◽  
Structural Model ◽  
Dynamic Programming Algorithm ◽  
Programming Method ◽  
Programming Algorithm ◽  
Dynamic Programming Method ◽  
Disparity Map ◽  
Matching Method ◽  
Multi Scale

The disparity map of dynamic programming method is poor. To overcome it, a stereo matching method based on multi-scale plane set is proposed in this paper. This method converts the structural model into the plane set. Define the key plane. Then the key planes are in a high-scale. The other planes are in the low scale. Stereo matching the multi-scale plane set using dynamic programming method. The experimental results show that: this method can solve the dynamic programming algorithm`s problem that disparity map has low matching accuracy and a lot of stripes error.

scholarly journals Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6188
Author(s):  
Ségolène Rogge ◽  
Ionut Schiopu ◽  
Adrian Munteanu
Keyword(s):  
Machine Learning ◽  
Light Field ◽  
Stereo Matching ◽  
Estimation Method ◽  
Similarity Index ◽  
Depth Estimation ◽  
Residual Error ◽  
Disparity Estimation ◽  
Machine Learning Techniques ◽  
Disparity Map

The paper presents a novel depth-estimation method for light-field (LF) images based on innovative multi-stereo matching and machine-learning techniques. In the first stage, a novel block-based stereo matching algorithm is employed to compute the initial estimation. The proposed algorithm is specifically designed to operate on any pair of sub-aperture images (SAIs) in the LF image and to compute the pair’s corresponding disparity map. For the central SAI, a disparity fusion technique is proposed to compute the initial disparity map based on all available pairwise disparities. In the second stage, a novel pixel-wise deep-learning (DL)-based method for residual error prediction is employed to further refine the disparity estimation. A novel neural network architecture is proposed based on a new structure of layers. The proposed DL-based method is employed to predict the residual error of the initial estimation and to refine the final disparity map. The experimental results demonstrate the superiority of the proposed framework and reveal that the proposed method achieves an average improvement of 15.65% in root mean squared error (RMSE), 43.62% in mean absolute error (MAE), and 5.03% in structural similarity index (SSIM) over machine-learning-based state-of-the-art methods.

scholarly journals Accurate hierarchical stereo matching based on 3D plane labeling of superpixel for stereo images from rovers

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142110021
Author(s):  
Haichao Li ◽  
Zhi Li ◽  
Jianbin Huang ◽  
Bo Meng ◽  
Zhimin Zhang
Keyword(s):  
Large Scale ◽  
Stereo Matching ◽  
State Of The Art ◽  
The State ◽  
Normal Vector ◽  
Stereo Images ◽  
Disparity Map ◽  
Matching Method ◽  
Search Range ◽  
Coarse To Fine

An accurate hierarchical stereo matching method is proposed based on continuous 3D plane labeling of superpixel for rover’s stereo images. This method can infer the 3D plane label of each pixel combined with the slanted-patch matching strategy and coarse-to-fine constraints, which is especially suitable for large-scale scene matching with low-texture or textureless regions. At every level, the stereo matching method based on superpixel segmentation makes the iteration convergence faster and avoids huge redundant computations. In the coarse-to-fine matching scheme, we propose disparity constraint and 3D normal vector constraint between adjacent levels through which the disparity map and 3D normal vector map at a coarser level are used to restrict the search range of disparity and normal vector at a fine level. The experimental results with the Chang’e-3 rover dataset and the KITTI dataset show that the proposed stereo matching method is efficiently and accurately compared with the state-of-the-art 3D labeling algorithm, especially in low-texture or textureless regions. The computational efficiency of this method is about five to six times faster than the state-of-the-art 3D labeling method, and the accuracy is better.

scholarly journals State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Jinxin Xu ◽  
Qingwu Li ◽  
Ying Luo ◽  
Yan Zhou ◽  
Jiayu Wang
Keyword(s):  
Stereo Matching ◽  
Three Dimensional ◽  
The State ◽  
Inspection System ◽  
Center Line ◽  
Disparity Map ◽  
Matching Method ◽  
Data Set ◽  
State Measurement ◽  
Distortion Rectification

To better monitor the state of isolating switches, an efficient binocular vision-based state measurement system is proposed in this article. Two optimal cameras are selected as the vision of our inspection system. Firstly, stereo calibration and distortion rectification are performed on acquired image pair. Secondly, to recover the three-dimensional information of switch, we propose a semi-global stereo matching method by using data- and structure-driven cost volume fusion and then optimizing raw disparity map with weighted- and edge discriminated-smoothness prior. Gradient content is enforced on the weight for suppressing small-weight-accumulation problem in weak-textured regions. Besides, Hough transform with feature constraints is implemented for removing the chaotic lines and extracting center line of the switch arm. Finally, based on the center line and corresponding disparity map of the switch arm, triangulation principle is used for calculating the true angle between the switch arm and insulator such that whether or not the isolating switch is fully closed can be detected. The experimental results demonstrate that the proposed stereo matching method can achieve good performance in Middlebury v.3 data set and switch images, and the system can precisely measure the state of switches.

scholarly journals A Semi-Supervised Monocular Stereo Matching Method

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 690
Author(s):  
Zhimin Zhang ◽  
Jianzhong Qiao ◽  
Shukuan Lin
Keyword(s):  
Stereo Matching ◽  
Ground Truth ◽  
Depth Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Stereo Pair ◽  
Single Image ◽  
Matching Method ◽  
Matching Model ◽  
Monocular Depth

Supervised monocular depth estimation methods based on learning have shown promising results compared with the traditional methods. However, these methods require a large number of high-quality corresponding ground truth depth data as supervision labels. Due to the limitation of acquisition equipment, it is expensive and impractical to record ground truth depth for different scenes. Compared to supervised methods, the self-supervised monocular depth estimation method without using ground truth depth is a promising research direction, but self-supervised depth estimation from a single image is geometrically ambiguous and suboptimal. In this paper, we propose a novel semi-supervised monocular stereo matching method based on existing approaches to improve the accuracy of depth estimation. This idea is inspired by the experimental results of the paper that the depth estimation accuracy of a stereo pair as input is better than that of a monocular view as input in the same self-supervised network model. Therefore, we decompose the monocular depth estimation problem into two sub-problems, a right view synthesized process followed by a semi-supervised stereo matching process. In order to improve the accuracy of the synthetic right view, we innovate beyond the existing view synthesis method Deep3D by adding a left-right consistency constraint and a smoothness constraint. To reduce the error caused by the reconstructed right view, we propose a semi-supervised stereo matching model that makes use of disparity maps generated by a self-supervised stereo matching model as the supervision cues and joint self-supervised cues to optimize the stereo matching network. In the test, the two networks are able to predict the depth map directly from a single image by pipeline connecting. Both procedures not only obey geometric principles, but also improve estimation accuracy. Test results on the KITTI dataset show that this method is superior to the current mainstream monocular self-supervised depth estimation methods under the same condition.

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