# Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
###### Abstract  
With the continuous advancement of industrialization and intelligentization, stereo-vision-based measurement technology for large-scale components has become a prominent research focus. To address weak-textured regions in large-scale component images and reduce mismatches in stereo matching, we propose a cross-scale multi-feature stereo matching algorithm. In the cost-computation stage, the sum of absolute differences (SAD), census, and modified census cost aggregation are employed as cost-calculation methods. During the cost-aggregation phase, cross-scale theory is introduced to fuse multi-scale cost volumes using distinct aggregation parameters through a cross-scale framework. Experimental results on both benchmark and real-world datasets demonstrate that the enhanced algorithm achieves an average mismatch rate of 12.25%, exhibiting superior robustness compared to conventional census transform and semi-global matching (SGM) algorithms. stereo matching; cross-scale cost fusion; machine vision +
Footnote †: journal: Applied sciences  
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computation methods can lead to significantly divergent matching outcomes. The cost-aggregation process serves as a central component in numerous algorithms [3], exerting substantial influence on the final results. Therefore, this study primarily focuses on the impacts of cost-computation and -aggregation processes on cross-scale measurement of components [4]. Among the various cost aggregation approaches, Gaussian filtering and mean filtering currently dominate linear filtering implementations, although these conventional methods often induce edge blurring artifacts at image boundaries [5]. [PERSON] et al. [6] proposed the guided image filtering algorithm, which demonstrates effective edge preservation, superior computational efficiency compared to the aforementioned methods, and maintains applicability in smooth image regions. This technique currently stands as a state-of-the-art edge-preserving filtering approach [7]. Owing to the computational efficiency and superior edge-preserving performance of guided image filtering, stereo matching algorithms leveraging this technique have garnered substantial research attention in recent years. In 2014, [PERSON] and [PERSON] et al. [8] introduced guided filtering for cost aggregation in local stereo matching algorithms. Leveraging the adaptive weighting mechanism of the guided filter, their method demonstrated superior performance on texture-rich image pairs. However, this approach employed simplistic cost computation and neglected application scenarios involving weakly textured stereo pairs. In 2012, [PERSON] et al. [9] proposed a novel cost-computation method by integrating SAD (sum of absolute differences) with gradient information. However, this approach merely performed a straightforward fusion of color intensity and gradient data, without incorporating the census transform cost, which demonstrates superior performance in weakly textured regions. The limitations of traditional stereo matching algorithms become particularly pronounced when processing images containing extensive weakly textured areas. The existing methods mentioned above are confined to single-scale image processing, neglecting multi-scale information. Their cost computation relies on limited feature representations and superficially applies guided filtering algorithms to the stereo matching stage. With no consideration of weakly textured images, these approaches exhibit inherent limitations, resulting in compromised matching accuracy in weakly textured regions. To address these limitations, this paper proposes an improved cross-scale guided filtering stereo matching algorithm for mechanical component measurement. The method first integrates hybrid cost computation combining SAD and modified census transform. A cross-scale guided filtering framework is adopted for cost aggregation, where scale-specific aggregation parameters are configured according to multi-scale feature characteristics.