Real-time stereo matching for depth estimation using GPU

Abstract

Real-time technology of stereo matching to generate depth map from stereo images is one of the important computer vision challenges in recent years. The depth map are used in various fields such as the robot binocular vision, 3D recognition system, 3D model reconstruction and so on. Because the initial depth estimation method cannot generate good results, most of the methods will accompany a refinement process afterwards. The aim of this paper is to improve the quality of initial depth map estimation without any depth refinement in real time via GPU. This paper proposes a real-time effective stereo matching using adaptive shape window on compute unified device architecture (CUDA). The proposed method estimate depth map by cost aggregation over shape-adaptive regions and generate the accurate initial depth map among the real-time methods by evaluating in Middlebury stereo benchmark.