Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion

Abstract

Semi-global matching (SGM) has been widely used in binocular vision. In spite of its good efficiency, SGM still has difficulties in dealing with low-texture regions. In this paper, an SGM algorithm based on multi-scale information fusion (MSIF), named SGM-MSIF, is proposed by combining multi-path cost aggregation and cross-scale cost aggregation (CSCA). Firstly, the stereo pairs at different scales are obtained by Gaussian pyramid down-sampling. The initial matching cost volumes at different scales are computed by combining census transform and color information. Then, the multi-path cost aggregation in SGM is introduced into the cost aggregation at each scale and the aggregated cost volumes are fused by CSCA. Thirdly, the disparity map is optimized by internal left-right consistency check and median filter. Finally, experiments are conducted on Middlebury datasets to evaluate the proposed algorithm. Experimental results show that the average error matching rate (EMR) of the proposed SGM-MSIF algorithm reduced by 1.96% compared with SGM. Compared with classical cross-scale stereo matching algorithm, the average EMR of SGM-MSIF algorithm reduced by 0.92%, while the processing efficiency increased by 58.7%. In terms of overall performance, the proposed algorithm outperforms the classic SGM and CSCA algorithms. It can achieve high matching accuracy and high processing efficiency for binocular vision applications, especially for those with low-texture regions.

A cascaded GRU-based stereoscopic matching network for precise plank measurement

“…Our synthetic dataset specifically targets industrial measurement environments, distinguishing it from existing synthetic datasets that encompass a wide range of scenes [9,37,42,44,49,50]. We employ the software Blender to generate simulated data [51], and each scene comprises images captured by stereo virtual camera, accompanied by disparity maps.…”

“…Nonetheless, calculating the correspondence, which is also referred to as computing disparity [3,4], for individual pixels across multiple images presents a challenging endeavor [5]. Traditional stereo matching algorithms, including BM [6], SGM [7], and their subsequent derivatives [8,9], encounter difficulties when confronted with discontinuous disparity, textureless region, repeating patterns and similar scenarios [10][11][12]. Furthermore, conventional matching methods necessitate individual computations for the optimal pairing of each pixel.…”

Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study

“…Nevertheless, there are numerous existing stereo-matching algorithms. To meet the requirements of accurate and fast stereo matching in complex scenes, this study selects the Semi-Global Block Matching algorithm (SGBM) [33] as the matching algorithm for bar-shaped obstacle features. At the same time, the SGBM stereo matching algorithm not only has high real-time performance and measurement accuracy but also possesses an efficient execution capability.…”

Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment

“…Bu et al [ 30 ] introduced a local edge-aware filtering method in SGM to enhance the interaction of adjacent scanlines and avoid fringe artifacts. Deng et al [ 31 ] combined multi-path cost aggregation and cross-scale cost aggregation (CSCA) to propose an SGM-MSIF algorithm based on multi-scale information fusion (MSIF). In order to achieve the high real-time performance of SGM algorithms, many scholars have combined SGM algorithms with FPGA.…”