RESUMEN
Abstract: 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 high-resolution 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 matching cost. We also design local patch surrounding the corresponding superpixel to accelerate our algorithm in parallel. The experimental result shows that our method achieves a good trade-off between running time and accuracy, including KITTI and Middlebury benchmark, which are the standard benchmarks for testing the stereo matching methods.