Stereo matching has been widely used in various computer vision applications and it is still a challenging problem. Adaptive support weights (ASW) methods represent the state of the art in stereo matching and have achieved outstanding performance. However, the local ASW methods fail to resolve the matching ambiguity in low texture areas because their cost aggregation is limited within local fixed or adaptive support windows. On the other hand, the non-local ASW methods perform cost aggregation along a special tree, so that these methods are often sensitive to high texture areas since some useful connectivity constrains between adjacent pixels are broken during constructing the special tree. To solve these problems, in this paper, a novel and generic fusing ASW framework are proposed for stereo matching. In this framework, we establish dual support windows for each pixel, i.e., a local window and the whole image window. As such, the primitive connectivity between each pixel and its neighboring pixels in the local window can be maintained, and then each pixel not only gets appropriate supports from neighboring pixels within its local support window but also receives more adaptive supports from the other pixels outside the local window. Furthermore, a local edge-aware filter and a non-local edge-aware filter, whose kernel windows correspond to the dual support windows, are merged in order to achieve collaborative filtering of the cost volume. The performance evaluation on the Middlebury and KITTI datasets shows that the proposed stereo matching method outperforms the current state-of-the-art methods.

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Stereo Matching With Fusing Adaptive Support Weights

This paper proposes a unified depth model based on the light field gradient, in which estimated disparity is represented by the complex number. The complex-valued disparity by the proposed depth model can be represented in both the Cartesian and polar coordinates. In the Cartesian representation, the proposed depth model is represented by real and imaginary parts of the disparity. The real part can be used for disparity estimation with respect to the in-focus plane, whereas the imaginary part represents the non-Lambertian-ness. In the polar representation, the proposed depth model is expressed by the disparity magnitude and disparity angle. The disparity magnitude shows the relationship among depth from stereo, depth from focus, and depth from defocus, whereas the disparity angle shows whether or not the bundles of rays are flipped with respect to the in-focus plane. For disparity analysis, we present the real response, imaginary response, magnitude response, and angle response, which are represented by the three-dimensional volume. Experimental results on synthetic and real light field images show that the real and magnitude responses of the proposed depth model are valid for local disparity estimation.

Complex-Valued Disparity: Unified Depth Model of Depth from Stereo, Depth from Focus, and Depth from Defocus Based on the Light Field Gradient

Stereo matching algorithm based on deep learning: A survey

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.

/pdf/local-stereo-matching-using-adaptive-cross-region-based-4td9pqn8dm.pdf

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

Guided image filtering (GIF) based cost aggregation or disparity refinement stereo matching algorithms are studied extensively owing to the edge-aware preserved smoothing property. However, GIF suffers from halo artifacts in sharp edges and shows high computational costs on high-resolution images. The performance of GIF in stereo matching would be limited by the above two defects. To solve these problems, a novel fast gradient domain guided image filtering (F-GDGIF) is proposed. To be specific, halo artifacts are effectively alleviated by incorporating an efficient multi-scale edge-aware weighting into GIF. With this multi-scale weighting, edges can be preserved much better. In addition, high computational costs are cut down by sub-sampling strategy, which decreases the computational complexity from O(N) to O(N/s2) (s: sub-sampling ratio) To verify the effectiveness of the algorithm, F-GDGIF is applied to cost aggregation and disparity refinement in stereo matching algorithms respectively. Experiments on the Middlebury evaluation benchmark demonstrate that F-GDGIF based stereo matching method can generate more accuracy disparity maps with low computational cost compared to other GIF based methods.

Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Deep self-guided cost aggregation for stereo matching

Cost aggregation benchmark for light field depth estimation

Motion deblurring is essential for reconstructing sharp images from given a blurry input caused by the camera motion. The complexity of this problem increases in a light field due to its depth-dependent blur constraint. A method of generating synthetic 3 degree-of-freedom (3-DOF) translation blur on a light field image without camera rotation has been introduced. In this study, we generate a camera translation and rotation (6-DOF) motion blur model that preserves the consistency of the light field image. Our experiment results show that the proposed blur model can maintain the parallax information (depth-dependent blur) in a light field image. Furthermore, we produce a synthetic blurry light field dataset based on the 6-DOF model. Finally, to validate the usability of the synthetic dataset, we conduct extensive benchmarking using state-of-the-art motion deblurring algorithms.

Williem

Papers

Deep self-guided cost aggregation for stereo matching

Cost aggregation benchmark for light field depth estimation

6-DOF motion blur synthesis and performance evaluation of light field deblurring