Based on the three-view analysis, this paper presents a new matching method for polyhedron reconstruction. The reconstruction process is driven by corners and edge line drawing. Under the requirement of the implicit condition of exact image-globe coordinate model, the matching methodology first uses the necessary condition of the corresponding-point pair, called the PCCF (point-correspondence constraint function), to find the second-order candidates of corresponding-corner triplets among the three images from corner-to-corner inspection. It then uses the local connected corner triplets (LCCTs) to select the first-order candidates. Lastly, it examines the real spatial position of each first-order candidate in the global coordinate system to confirm the exact correspondence. The method is simple and reliable and can be applied to any arbitrary configuration of three camera locations. To accomplish partial and complete polyhedron reconstruction, a feature extraction method is presented. The method is fast and most suitable for noisy data. Experimental results are included in the paper to verify our proposed methods. The measurement error is less than 2.2 mm (0.3%) when the distance between the camera and the object is about 700 mm.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence