Stereo Matching via Disparity Estimation and Surface Modeling

2007 ◽  
Author(s):  
Jong Dae Oh ◽  
Siwei Ma ◽  
C.-C. Jay Kuo
Keyword(s):  
Stereo Matching ◽  
Surface Modeling ◽  
Disparity Estimation

scholarly journals An Edge-Sense Bidirectional Pyramid Network for Stereo Matching of VHR Remote Sensing Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4025
Author(s):  
Rongshu Tao ◽  
Yuming Xiang ◽  
Hongjian You
Keyword(s):  
Remote Sensing ◽  
Stereo Matching ◽  
Tall Buildings ◽  
Disparity Estimation ◽  
Complex Structures ◽  
Learning Networks ◽  
Remote Sensing Images ◽  
Essential Step ◽  
Disparity Range ◽  
The Cost

As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.

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.

scholarly journals Improving stereo matching algorithm with adaptive cross-scale cost aggregation

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775154 ◽  
Author(s):  
Cong Bai ◽  
Qing Ma ◽  
Pengyi Hao ◽  
Zhi Liu ◽  
Jinglin Zhang
Keyword(s):  
Stereo Matching ◽  
Disparity Estimation ◽  
Data Sets ◽  
Human Beings ◽  
Stereo Correspondence ◽  
Matching Accuracy ◽  
Automatic Driving ◽  
Cost Aggregation ◽  
Correspondence Matching ◽  
Consistency Constraints

Human beings process stereoscopic correspondence across multiple purposes like robot navigation, automatic driving, and virtual or augmented reality. However, this bioinspiration is ignored by state-of-the-art dense stereo correspondence matching methods. Cost aggregation is one of the critical steps in the stereo matching method. In this article, we propose an optimized cross-scale cost aggregation scheme with coarse-to-fine strategy for stereo matching. This scheme implements cross-scale cost aggregation with the smoothness constraint on neighborhood cost, which essentially extends the idea of the inter-scale and intra-scale consistency constraints to increase the matching accuracy. The neighborhood costs are not only used in the intra-scale consistency to ensure that the regularized costs vary smoothly in an eight-connected neighbors region but also incorporated with inter-scale consistency to optimize the disparity estimation. Additionally, the improved method introduces an adaptive scheme in each scale with different aggregation methods. Finally, experimental results evaluated both on classic Middlebury and Middlebury 2014 data sets show that the proposed method performs better than the cross-scale cost aggregation. The whole stereo correspondence algorithm achieves competitive performance in terms of both matching accuracy and computational efficiency. An extensive comparison, including the KITTI benchmark, illustrates the better performance of the proposed method also.

Near-real-time stereo matching with slanted surface modeling and sub-pixel accuracy

2011 ◽  
Vol 44 (10-11) ◽  
pp. 2701-2710 ◽  
Author(s):  
Minglun Gong ◽  
Yilei Zhang ◽  
Yee-Hong Yang
Keyword(s):  
Real Time ◽  
Stereo Matching ◽  
Surface Modeling

OCCLUSION-AIDED SUPPORT WEIGHTS FOR LOCAL STEREO MATCHING

2012 ◽  
Vol 26 (03) ◽  
pp. 1255007 ◽  
Author(s):  
WEI WANG ◽  
CAIMING ZHANG ◽  
SHUOZHEN WANG ◽  
XUEMEI LI
Keyword(s):  
Stereo Matching ◽  
Experimental Results ◽  
Disparity Estimation ◽  
Consistency Check ◽  
Negative Effects ◽  
Support Weight ◽  
Cost Aggregation ◽  
Depth Discontinuities ◽  
Local Methods ◽  
Adaptive Support

There has been a significant improvement in stereo matching with the introduction of adaptive support weights. Existing local methods mainly focus on the computation of support weight which is critical in cost aggregation and usually get excellent results. However, the negative effects of occluded regions are often ignored, which results in the problem of foreground fattening and blurred depth borders. This paper proposes a novel support aggregation strategy by utilizing the occlusion information obtained from left-right consistency check. The weights of invalid points are noticeably reduced at each disparity estimation stage. Experimental results on the Middlebury images show that our method is highly effective in improving the disparities of points around occluded areas and depth discontinuities. According to the Middlebury benchmark, the proposed method achieves the best performance among all the local methods. Moreover, our approach can be easily integrated into nearly all the existing support weights strategies.

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