It is well known that preserving depth edges is an effective solution for achieving the accurate disparity map in stereo matching, but many state-of-the-art methods do not preserve depth edges well. In order to solve it efficiently, the cell structure containing irregular and regular shape regions is designed to preserve depth edges. Based on the well-designed cell structure, a novel disparity estimation method for stereo matching is proposed, in which a two-layer disparity optimization method is proposed to refine the disparity plane; it includes the front-parallel disparities computation and slanted-surfaces disparity plane refinement. In the framework of front-parallel disparities computation, a tree-based cost aggregation method is presented to make full use of the segmentation information of cells and then performing semi-global cost aggregation. In the framework of slanted-surfaces disparity plane refinement, a new probability model is proposed that employs Bayesian inference for refining disparities in textureless, weak texture and occluded regions. Experimental results show that higher accuracy could be achieved via the proposed method compared with some known state-of-the-art stereo methods on KITTI 2015 and Middlebury dataset, which are the standard benchmarks for testing the stereo matching methods. It can also be indicated that the proposed method can produce accurate disparity map and have good generalization performance.
Obtaining the accurate disparity of each pixel quickly is the goal of stereo matching, but it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for highresolution images. We present an novel two-stage optimization strategy to get the accurate disparity map for highresolution stereo image efficiently, which includes feature points optimization and superpixel optimization. In the first stage, we construct the support points including edge points and robust points for triangulation, which is used to extract feature points and then perform spatial propagation and random refinement to get the candidate 3D label sets. In the stage of superpixel optimization, we update per pixel labels of the corresponding superpixels using the candidate label sets, and then perform spatial propagation and random refinement. In order to provide more prior information to identify weak texture and textureless areas, we design the weight combination of "intensity + gradient + binary image" for constructing an optimal minimum spanning tree (MST) to compute the aggregated matching cost, and obtain the labels of minimum aggregated HIGHLIGHTS• A new method of extracting the feature points in superpixels is proposed to compute the candidate disparities using non-local cost aggregation.• A novel two-stage optimization framework for superpixel proposals by first getting the labels of feature points quickly and then consists the candidate label sets for updating the labels of pixels within the corresponding superpixel instead of assigning a label randomly to those pixels.• The weight combination of intensity, gradient and binary image is designed for constructing an optimal minimum spanning tree to compute the aggregated matching cost.
Occlusion area detection is a crucial step affecting the performance of the binocular stereo matching algorithm, but the traditional method of occlusion area detection has two major problems, including left–right consistency detection (LRC). First, these algorithms must obtain the left and right disparity maps with precision. Second, these algorithms cannot detect the occlusion region at the image’s borders. We propose the single view occlusion area detective (SVOAD) algorithm to detect these occlusion areas and better deal with them. The SVOAD can detect the area of occlusion from a single image, thereby reducing the computational cost. Additionally, the algorithm can detect the occlusion region in all image regions. This paper also improves the guided filter so that it works better with the end-to-end neural network and makes the SVOAD algorithm work better.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.