Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
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 prop...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/5837 |
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| Summary: | 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. |
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| ISSN: | 2076-3417 |