Improved Stereo Matching Algorithm Based on Multi-Scale and Multi-Feature

Abstract

In the field of binocular stereo vision, stereo matching is an important research direction. In order to solve the problem that some stereo matching algorithms have high error matching rate in the weak texture region, this paper proposes a stereo matching algorithm based on multi-scale and multi feature. The STAD, gradient and improved Census cost fusion are used as the cost computing method. In the cost aggregation stage, take the guided filtering algorithm as the core. The cost cubes of different scales are fused using the idea of cross scales, and different cost aggregation parameters are set for the cost cubes of different scales. For some errors in disparity map results, a variety of methods of disparity post-processing are used. The experimental results show the accuracy of the algorithm in the weak texture area. The experimental results of standard image pairs on the Mid-dlebury 3.0 test platform show that the average mismatch rate of the algorithm in multiple groups of weak texture images is 8.16%, which has higher matching accuracy than the traditional SGM and other algorithms.