关键词: PMVS算法, Universal-RANSAC, 双约束条件, 多视立体重建

Abstract:  The PMVS (patch-based multi-view stereo) algorithm is widely used in the multi-view stereo field because of its good performance. However, the surface details of the reconstructed model are lost and the exact location of the reconstruction points is not accurate. These problems are very serious in some cases, especially while the input images are few and the texture of the reconstructed model is less. In order to solve these problems, this paper studies the removal of mismatched candidate points and the ranking of seed point reliability: firstly, use USAC (Universal-RANSAC) to remove the candidate matching error; then, put forward the double constraint strategy, meanwhile select the candidate space point which has high reliability as seed. The details of the reconstruction model fit to the original object more. Meanwhile the number of reconstruction points in the model with less texture has been significantly increased and the holes of the texture model are obviously reduced. The validity of the improved algorithm is proven to be more effective and practical.

Key words:  PMVS algorithm, Universal-RANSAC, double constraints, multi-view stereo reconstruction