Self-Adaptive Matching In Local Windows For Depth Estimation

This paper proposes a novel local stereo matching approach based on self-adapting matching window. We improve the accuracy of stereo matching in 3 steps. First, we integrate shape and size information, and construct robust minimum matching windows by applying a self-adapting method. Then, two matching cost optimization strategies are employed for handling both occlusion regions and image borders. Last, we perform a refinement algorithm for obtaining more accurate depth map. Experiment results on the Middlebury stereo image pairs prove that the proposed matching method performs equally well in comparison with other state-of-the-art local approaches.