scholarly journals Local Stereo Matching Using Adaptive Local Segmentation

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Sanja Damjanović ◽  
Ferdinand van der Heijden ◽  
Luuk J. Spreeuwers
Keyword(s):  
Stereo Matching ◽  
Disparity Estimation ◽  
Dynamic Threshold ◽  
Gray Level ◽  
Test Bed ◽  
Tomato Seedling ◽  
Local Algorithms ◽  
Reconstruction Quality ◽  
Stereo Reconstruction ◽  
Local Intensity

We propose a new dense local stereo matching framework for gray-level images based on an adaptive local segmentation using a dynamic threshold. We define a new validity domain of the frontoparallel assumption based on the local intensity variations in the 4 neighborhoods of the matching pixel. The preprocessing step smoothes low-textured areas and sharpens texture edges, whereas the postprocessing step detects and recovers occluded and unreliable disparities. The algorithm achieves high stereo reconstruction quality in regions with uniform intensities as well as in textured regions. The algorithm is robust against local radiometrical differences and successfully recovers disparities around the objects edges, disparities of thin objects, and the disparities of the occluded region. Moreover, our algorithm intrinsically prevents errors caused by occlusion to propagate into nonoccluded regions. It has only a small number of parameters. The performance of our algorithm is evaluated on the Middlebury test bed stereo images. It ranks highly on the evaluation list outperforming many local and global stereo algorithms using color images. Among the local algorithms relying on the frontoparallel assumption, our algorithm is the best-ranked algorithm. We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face.

Hilbert stereo reconstruction algorithm based on depth feature and stereo matching

2020 ◽  
Vol 39 (5) ◽  
pp. 8027-8038
Author(s):  
Weiyi Kong ◽  
Menglong Yang ◽  
Qinzhen Huang
Keyword(s):  
High Performance ◽  
Stereo Matching ◽  
Texture Features ◽  
Reconstruction Algorithm ◽  
Feature Representation ◽  
Disparity Estimation ◽  
Data Set ◽  
Stereo Reconstruction ◽  
Hilbert Coefficient ◽  
Depth Feature

This paper proposes a Hilbert stereo reconstruction algorithm based on depth feature and stereo matching to solve the problem of occlusive region matching errors, namely, the Hilbert stereo network. The traditional stereo network pays more attention to disparity itself, leading to the inaccuracy of disparity estimation. Our design network studies the effective disparity matching and refinement through reconstruction representation of Hilbert’s disparity coefficient. Since the Hilbert coefficient is not affected by the occlusion and texture in the image, stereo disparity matching can conducted effectively. Our network includes three sub-modules, namely, depth feature representation, Hilbert cost volume fusion, and Hilbert refinement reconstruction. Separately, texture features of different depth levels of the image were extracted through Hilbert filtering operation. Next, stereoscopic disparity fusion was performed, and then Hilbert designed to refine the difference regression stereo matching solution was used. Based on the end-to-end design, the structure is refined by combining the depth feature extraction module and Hilbert coefficient disparity. Finally, the Hilbert stereo matching algorithm achieves excellent performance on standard big data set and is compared with other advanced stereo networks. Experiments show that our network has high accuracy and high performance.

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 Cloud photogrammetry with dense stereo for fisheye cameras

2016 ◽  
Vol 16 (22) ◽  
pp. 14231-14248 ◽  
Author(s):  
Christoph Beekmans ◽  
Johannes Schneider ◽  
Thomas Läbe ◽  
Martin Lennefer ◽  
Cyrill Stachniss ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Geometric Constraints ◽  
Large Field ◽  
Cloud Base ◽  
Strong Wind ◽  
Real World Data ◽  
First Case ◽  
Novel Approach ◽  
Stereo Reconstruction ◽  
Fisheye Lenses

Abstract. We present a novel approach for dense 3-D cloud reconstruction above an area of 10 × 10 km2 using two hemispheric sky imagers with fisheye lenses in a stereo setup. We examine an epipolar rectification model designed for fisheye cameras, which allows the use of efficient out-of-the-box dense matching algorithms designed for classical pinhole-type cameras to search for correspondence information at every pixel. The resulting dense point cloud allows to recover a detailed and more complete cloud morphology compared to previous approaches that employed sparse feature-based stereo or assumed geometric constraints on the cloud field. Our approach is very efficient and can be fully automated. From the obtained 3-D shapes, cloud dynamics, size, motion, type and spacing can be derived, and used for radiation closure under cloudy conditions, for example. Fisheye lenses follow a different projection function than classical pinhole-type cameras and provide a large field of view with a single image. However, the computation of dense 3-D information is more complicated and standard implementations for dense 3-D stereo reconstruction cannot be easily applied. Together with an appropriate camera calibration, which includes internal camera geometry, global position and orientation of the stereo camera pair, we use the correspondence information from the stereo matching for dense 3-D stereo reconstruction of clouds located around the cameras. We implement and evaluate the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with reflectivity observations measured by a cloud radar and the cloud-base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus evolution in the presence of strong wind shear.

Local Stereo Matching: An Adaptive Weighted Guided Image Filtering-Based Approach

2020 ◽  
pp. 2154010
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Stereo Matching ◽  
Stringent Response ◽  
Image Filtering ◽  
Absolute Difference ◽  
Map Building ◽  
Disparity Map ◽  
Vision Systems ◽  
Local Algorithms ◽  
Gradient Algorithms ◽  
Guided Image Filtering

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.

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.

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