Real Time Stereo Matching Using Two Step Zero-Mean SAD and Dynamic Programing

Dense depth map extraction is a dynamic research field in a computer vision that tries to recover three-dimensional information from a stereo image pair. A large variety of algorithms has been developed. The local methods based on block matching that are prevalent due to the linear computational complexity and easy implementation. This local cost is used on global methods as graph cut and dynamic programming in order to reduce sensitivity to local to occlusion and uniform texture. This paper proposes a new method for matching images based on a two-stage of block matching as local cost function and dynamic programming as energy optimization approach. In our work introduce the two stage of the zero-mean sum of absolute differences (ZSAD) combined with dynamic programming: the smoothness and ordering constraints are used to optimize correspondences. Stereo matching accuracy and runtime are the fundamental metrics to evaluate the stereo matching methods. The real-time has become a reality through the complexity reduction of the calculation and the use of parallel high-performance graphics hardware. In this paper we evaluate the developed method on using Middlebury stereo benchmark and, we propose a GPU CUDA implementation in order to accelerate our algorithm and reach the real time.

I. Introduction

The dense correspondence between a stereo image pair and 3D reconstitution are a key problem for computer vision. They consist in finding in a stereo image the pairs of pixels which are the projections of the same point of the scene. The reconstitution of 3D information from a two images of the same scene taken from distinct viewpoints is the main objective of stereo systems. The path taken from the acquisition of the two stereo images to the 3D reconstruction contains three fundamental steps: calibration, correspondence and 3D reconstruction.