LESC: Superpixel cut‐based local expansion for accurate stereo matching

Abstract

The rapid estimation of the accurate disparity between pixels is the goal of stereo matching. However, it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for high-resolution images. In this paper, a novel superpixel cut-based method is proposed, in an attempt to get the accurate disparity map efficiently, including the multi-layer superpixel optimization and iteractive local α-expansion in parallel. As for the multi-layer superpixel optimization, feature point optimization is designed to get accurate candidate labels that are set for most pixels using non-local cost aggregation strategy and update per-pixel labels of the corresponding superpixels from the candidate label sets on the small-size superpixel layer, and then update the middle to large-size superpixel layers progressively using non-local cost aggregation strategy. In order to provide more prior information to identify weak texture and textureless regions in non-local cost aggregation, the weight combination of “intensity + gradient + binary image” is proposed for constructing an optimal minimum spanning tree (MST) to calculate the aggregated matching cost and obtain the labels of minimum aggregated matching cost. Moreover, the local patch surrounding the corresponding superpixels is designed to accelerate superpixel optimization in parallel, and a neighborhood structure is presented to optimize the algorithm in this study, including superpixel neighborhood and patch neighborhood. As for the iteractive local α-expansion, three layers of patch structure corresponding to the superpixel neighborhood structure is proposed for optimizing the algorithm in this study. The experimental results show that higher accuracy could be achieved via the method in this study compared with some known state-of-the-art stereo methods on KITTI 2015 and Middlebury benchmark V3, which are the standard benchmarks for testing the stereo matching methods.