scholarly journals Effective Stereo Matching with Segment-based Cost Aggregation and Dual-path Refinement

2020 ◽  
Author(s):  
Chih-Shuan Huang ◽  
Ya-Han Huang ◽  
Din-Yuen Chan ◽  
Jar-Ferr Yang
Keyword(s):  
Stereo Matching ◽  
Texture Features ◽  
Spatial Proximity ◽  
Depth Maps ◽  
Cost Aggregation ◽  
Main Challenge ◽  
Color Similarity ◽  
Matching Cost ◽  
Iterative Procedures ◽  
Selection Of

Abstract Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The main challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded and discontinuous regions. To solve the aforementioned problems, in this paper, the proposed robust stereo matching system by using segment-based superpixels and magapixels to design adaptive stereo matching computation and dual-path refinement. After determination for edge and smooth regions and selection of matching cost, we suggest the segment–based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements utilize the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for left-most and right most regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results demonstrate that the proposed system can obtain higher accurate depth maps compared with the conventional methods.

scholarly journals Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Chih-Shuan Huang ◽  
Ya-Han Huang ◽  
Din-Yuen Chan ◽  
Jar-Ferr Yang
Keyword(s):  
Stereo Matching ◽  
Texture Features ◽  
Spatial Proximity ◽  
Depth Maps ◽  
Cost Aggregation ◽  
Color Similarity ◽  
Matching Cost ◽  
Iterative Procedures ◽  
Selection Of

Abstract Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded, and discontinuous regions. In this paper, the proposed stereo matching system uses segment-based superpixels and matching cost. After determination of edge and smooth regions and selection of matching cost, we suggest the segment-based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements use the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for leftmost and rightmost regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system can achieve higher accurate depth maps than the conventional methods.

scholarly journals Shape Reserved Stereo Matching with Segment-based Cost Aggregation and Dual-path Refinement

2020 ◽  
Author(s):  
Chih-Shuan Huang ◽  
Ya-Han Huang ◽  
Din-Yuen Chan ◽  
Jar-Ferr Yang
Keyword(s):  
Computer Vision ◽  
Stereo Matching ◽  
Texture Features ◽  
Depth Estimation ◽  
Spatial Proximity ◽  
Depth Maps ◽  
Cost Aggregation ◽  
Color Similarity ◽  
Adaptive Support ◽  
Selection Of

Abstract Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various smart applications. The major challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded and discontinuous regions. In this paper, we propose a robust stereo matching system, which is based on segment-based superpixels, to design adaptive matching computation and dual-path refinement. After the selection of matching costs, we suggest the segment-based adaptive support weights for cost aggregation, instead of color similarity and spatial proximity, to achieve precise depth estimation. Then, the proposed dual-path depth refinement, which refers the texture features in a cross-based support region, corrects the inaccurate disparities to successively refine the depth maps with shape reserving. Specially for left-most and right most regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results show that the proposed system achieves higher accurate depth maps than the conventional stereo matching methods.

scholarly journals Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Lingyin Kong ◽  
Jiangping Zhu ◽  
Sancong Ying
Keyword(s):  
Stereo Matching ◽  
Computing Time ◽  
Image Filtering ◽  
Computation Method ◽  
Aggregation Method ◽  
Additional Time ◽  
Matching Algorithm ◽  
Cost Aggregation ◽  
Matching Cost ◽  
Guided Image Filtering

Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.

scholarly journals Local stereo matching algorithm using modified dynamic cost computation

2021 ◽  
Vol 22 (3) ◽  
pp. 1312
Author(s):  
A. F. Kadmin ◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
T. F. Tg. Wook
Keyword(s):  
Stereo Matching ◽  
Depth Information ◽  
Computation Method ◽  
Local Method ◽  
Matching Algorithm ◽  
Depth Measurement ◽  
Cost Aggregation ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost

Stereo matching is an essential subject in stereo vision architecture. Traditional framework composition consists of several constraints in stereo correspondences such as illumination variations in images and inadequate or non-uniform light due to uncontrollable environments. This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. This approach utilised modified dynamic cost computation in the matching cost. A modified census transform with dynamic histogram is used to provide the cost in the cost computation. The algorithm applied the fixed-window strategy with bilateral filtering to retain image depth information and edge in the cost aggregation stage. A winner takes all (WTA) optimisation and left-right check with adaptive bilateral median filtering are employed for disparity refinement. Based on the Middlebury benchmark dataset, the algorithm developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.

scholarly journals Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images

2020 ◽  
Vol 10 (5) ◽  
pp. 1869
Author(s):  
Hua Liu ◽  
Rui Wang ◽  
Yuanping Xia ◽  
Xiaoming Zhang
Keyword(s):  
Cost Function ◽  
Stereo Matching ◽  
Guided Filter ◽  
Disparity Map ◽  
Cost Aggregation ◽  
Dense Stereo Matching ◽  
Gradient Based ◽  
Matching Cost ◽  
Cost Measurement ◽  
Image Pairs

Dense stereo matching has been widely used in photogrammetry and computer vision applications. Even though it has a long research history, dense stereo matching is still challenging for occluded, textureless and discontinuous regions. This paper proposed an efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions. At first, an efficient matching cost function combining enhanced image gradient-based matching cost and improved census transform-based matching cost is introduced. This proposed matching cost function is robust against radiometric variations and textureless regions. Following this, an adaptive shape cross-based window is constructed for each pixel and a modified guided filter based on this adaptive shape window is implemented for cost aggregation. The final disparity map is obtained after disparity selection and multiple steps disparity refinement. Experiments were conducted on the Middlebury benchmark dataset to evaluate the effectiveness of the proposed cost measurement and cost aggregation strategy. The experimental results demonstrated that the average matching error rate on Middlebury standard image pairs is 9.40%. Compared with the traditional guided filter-based stereo matching method, the proposed method achieved a better matching result in textureless regions.

Dense Disparity Computing Method Based on Mesh Aggregation and Snake Optimization for Stereo Vision

2020 ◽  
Vol 13 (3) ◽  
pp. 95-112
Author(s):  
Liu Shuang ◽  
Yu Shuchun
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Cost Calculation ◽  
Segmentation Method ◽  
Suppression Effect ◽  
Cost Aggregation ◽  
Brightness Difference ◽  
Matching Cost ◽  
Computing Method ◽  
Snake Algorithm

In order to generate continuous and dense disparity images, a stereo matching method based on mesh aggregation and Snake optimization is proposed in this article. First, the reference pixels are obtained, so as to improve the suppression effect of the brightness difference in Census transform and improve the accuracy of initial matching cost calculation. Second, the image is divided by SLIC super pixel segmentation method, and the neighborhood pixels are searched according to the mesh search in the region, and the matching cost of these pixels are aggregated together according to the corresponding weight to complete cost aggregation of the pixels to be matched. Third, the Snake algorithm is used in optimizing the boundary of the disparity region. Eight classes of images on the Middlebury platform are selected as the test images, and the four algorithms on the Middlebury platform are selected as reference algorithms to carry out the experimental research. The experimental results show that proportion to bad pixels is low and disparity is continuous and dense on the disparity image calculated by the algorithm proposed in this article. Performance of the proposed method is close to LocalExp algorithm which is the best on the Middlebury platform, and the proposed method can be better applied in the stereo vision.

scholarly journals The Design and Implementation of Postprocessing for Depth Map on Real-Time Extraction System

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwei Tang ◽  
Bin Li ◽  
Huosheng Li ◽  
Zheng Xu
Keyword(s):  
Real Time ◽  
Stereo Matching ◽  
Depth Map ◽  
Depth Estimation ◽  
Consistency Check ◽  
Key Technology ◽  
Design And Implementation ◽  
Depth Maps ◽  
Matching Cost

Depth estimation becomes the key technology to resolve the communications of the stereo vision. We can get the real-time depth map based on hardware, which cannot implement complicated algorithm as software, because there are some restrictions in the hardware structure. Eventually, some wrong stereo matching will inevitably exist in the process of depth estimation by hardware, such as FPGA. In order to solve the problem a postprocessing function is designed in this paper. After matching cost unique test, the both left-right and right-left consistency check solutions are implemented, respectively; then, the cavities in depth maps can be filled by right depth values on the basis of right-left consistency check solution. The results in the experiments have shown that the depth map extraction and postprocessing function can be implemented in real time in the same system; what is more, the quality of the depth maps is satisfactory.

scholarly journals A Joint 2D-3D Complementary Network for Stereo Matching

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1430
Author(s):  
Xiaogang Jia ◽  
Wei Chen ◽  
Zhengfa Liang ◽  
Xin Luo ◽  
Mingfei Wu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Computational Cost ◽  
Research Field ◽  
Disparity Map ◽  
Improve Performance ◽  
Cost Aggregation ◽  
Disparity Range ◽  
Public Datasets ◽  
Coarse To Fine ◽  
Speed And Accuracy

Stereo matching is an important research field of computer vision. Due to the dimension of cost aggregation, current neural network-based stereo methods are difficult to trade-off speed and accuracy. To this end, we integrate fast 2D stereo methods with accurate 3D networks to improve performance and reduce running time. We leverage a 2D encoder-decoder network to generate a rough disparity map and construct a disparity range to guide the 3D aggregation network, which can significantly improve the accuracy and reduce the computational cost. We use a stacked hourglass structure to refine the disparity from coarse to fine. We evaluated our method on three public datasets. According to the KITTI official website results, Our network can generate an accurate result in 80 ms on a modern GPU. Compared to other 2D stereo networks (AANet, DeepPruner, FADNet, etc.), our network has a big improvement in accuracy. Meanwhile, it is significantly faster than other 3D stereo networks (5× than PSMNet, 7.5× than CSN and 22.5× than GANet, etc.), demonstrating the effectiveness of our method.

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