Stereo matching method based on high-precision occlusion-recovering and discontinuity-preserving

Abstract

Stereo matching is the core of machine vision, such as 3D reconstruction, posture perception and driverless technology in the future etc. Currently, there are still great difficulties in improving the matching accuracy of disparity discontinuity and occlusion regions. In this paper, we propose a new high-precision occlusion-recovering and discontinuity-preserving method for stereo matching. We focus on disparity refinement, and present an adaptability-based disparity reconstruction that achieves accurate occlusion-recovering by fusing color aberration, distance, and disparity value. For discontinuity-preserving, disparity-based secondary guided filtering is proposed, then introduce adaptive image enhancement and adaptive Canny edge detection to correct errors in discontinuous regions. The experiments on Middlebury standard dataset show that our algorithm has high overall accuracy, especially in the disparity discontinuity and occlusion regions. This novel method is more direct, efficient, and less sensitive to the accuracy of disparity estimation, and the live-action shooting shows the robustness of our algorithm. The method has the prospect of civil and engineering use, and presents scientific reference for high-precision target identification, target tracking, 3D measurement, etc.