scholarly journals Spatial‐Tree Filter for Cost Aggregation in Stereo Matching

2021 ◽  
Author(s):  
Yusheng Jin ◽  
Hong Zhao ◽  
Penghui Bu
Keyword(s):  
Stereo Matching ◽  
Cost Aggregation ◽  
Spatial Tree

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.

Histogram‐based cost aggregation strategy with joint bilateral filtering for stereo matching

2016 ◽  
Vol 10 (3) ◽  
pp. 173-181 ◽  
Author(s):  
Limei Fu ◽  
Guohua Peng ◽  
Weijie Song
Keyword(s):  
Stereo Matching ◽  
Bilateral Filtering ◽  
Cost Aggregation

Cross-scale cost aggregation integrating intrascale smoothness constraint with weighted least squares in stereo matching

2017 ◽  
Vol 34 (4) ◽  
pp. 648 ◽  
Author(s):  
Hao Ma ◽  
Shunyi Zheng ◽  
Chang Li ◽  
Yingsong Li ◽  
Li Gui ◽  
...  
Keyword(s):  
Least Squares ◽  
Stereo Matching ◽  
Weighted Least Squares ◽  
Cost Aggregation ◽  
Smoothness Constraint

Automatic parameter estimation based on the degree of texture overlapping in accurate cost-aggregation stereo matching for three-dimensional video display

2015 ◽  
Vol 54 (29) ◽  
pp. 8678 ◽  
Author(s):  
Nan Guo ◽  
Xinzhu Sang ◽  
Duo Chen ◽  
Peng Wang ◽  
Songlin Xie ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Video Display ◽  
Cost Aggregation

Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

2013 ◽  
Vol 23 (7) ◽  
pp. 1119-1130 ◽  
Author(s):  
Cuong Cao Pham ◽  
Jae Wook Jeon
Keyword(s):  
Stereo Matching ◽  
Cost Aggregation ◽  
Domain Transformation

Joint Histogram-Based Cost Aggregation for Stereo Matching

2013 ◽  
Vol 35 (10) ◽  
pp. 2539-2545 ◽  
Author(s):  
Dongbo Min ◽  
Jiangbo Lu ◽  
M. N. Do
Keyword(s):  
Stereo Matching ◽  
Cost Aggregation ◽  
Joint Histogram

Edge-preserving guided filtering based cost aggregation for stereo matching

2016 ◽  
Vol 39 ◽  
pp. 107-119 ◽  
Author(s):  
Shiqiang Zhu ◽  
Zhi Wang ◽  
Xuequn Zhang ◽  
Yuehua Li
Keyword(s):  
Stereo Matching ◽  
Edge Preserving ◽  
Cost Aggregation ◽  
Guided Filtering

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 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.

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