High-precision edge-preserving stereo matching for cabinet panels using Markov random fields with guided image filtering

Quality control is critical in cabinet panel manufacturing due to the complexity of the assembly process, which requires three-dimensional measurement methods for enhanced precision and efficiency compared to conventional two-dimensional techniques. Stereo vision offers an effective solution with high accuracy, efficiency, and cost-effectiveness, yet challenges like unclear edge disparities, occlusions, and weak textures persist. To overcome these, we propose a high-precision stereo reconstruction method combining guided image filtering with Markov random fields. Simulated and real-world experiments validate our approach, demonstrating significant improvements in challenging scenarios. This work aims to advance stereo vision's practical application in manufacturing.