# Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
## 4 Conclusions  
To address the challenges of extensive weakly textured regions and low stereo matching accuracy in large-scale components, this paper proposes an improved cross-scale multi-feature fusion stereo matching algorithm designed for practical industrial deployment. Although neural-network-based methods dominate current research, their reliance on large datasets and high-end hardware to some extent limits their further development in industrial environments. The developed method strategically enhances the traditional census transform through a hardware-compatible adaptive threshold mechanism. By calculating the mean value of all surrounding pixels and dynamically replacing the central pixel value based on statistical deviation analysis, this data-efficient approach significantly improves matching precision without requiring computationally intensive training procedures.  
The multi-scale optimization framework further demonstrates the industrial implementation advantages through the adaptive parameter selection of different image scales. Compared with neural methods that require complete retraining of the new environment, our guided filtering-based cost aggregation and inter-scale regularization maintain operational stability while reducing the mismatch rate between datasets to 12.25%. Under standard lighting conditions, this improved method retains edge consistency and structural integrity more effectively than the traditional SGM method, thereby enhancing the fidelity of point cloud reconstruction.  
The compatibility of this algorithm with hardware-aware optimization has established a reliable foundation for industrial measurement, especially in the dimensional measurement of large mechanical components, where predictable performance exceeds the theoretical advantages of neural-network-based methods. The subsequent work will continue to study  
Figure 9: Mechanical parts: (**a**) left image; (**b**) disparity of stereo matching by SGM; (**c**) disparity of stereo matching in this paper.  
the performance of this method under extreme conditions such as strong light. Under the core principle of maintaining the minimum dependence on the data set, the accuracy and operational flexibility of actual industrial production will be further improved.  
Conceptualization, N.C. and D.S.; methodology, N.C. and P.Z.; software, N.C.; validation, N.C. and D.S.; formal analysis, N.C. and P.Z.; investigation, N.C.; resources, D.S. and P.Z.; data curation, N.C.; writing--original draft preparation, N.C.; writing--review and editing, D.S. and P.Z.; visualization, N.C.; supervision, D.S. and P.Z.; project administration, D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key R&D Plan of Shandong Province Major Science and Technology Innovation Project (no. 2023 CXGC010701) and the 2024 City-University Integration Development Strategic Engineering Project (no. JNSX2024066).  
Not applicable.  
Data Availability StatementData available upon request due to privacy. The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.