3D reconstruction based on KAZE quasi-dense match algorithm
Meng Zhang · 2022
The traditional matching algorithm is easy to lose the detail information in the complex background, which is disadvantageous to the 3D reconstruction. To solve these problems, a quasi-dense algorithm based on KAZE feature detection is proposed in this paper, which is helpful to more accurate 3D reconstruction. Firstly, the method uses the KAZE Algorithm to extract the feature points, constructs the feature vector to match, and uses the random sample consistency algorithm to remove the mismatched points. Then the region growth is realized by the polar distance constraint criterion based on the adaptive window diffusion method. The experimental results show that the proposed algorithm can get satisfactory quasi- dense matching results and good disparity map, and can reflect the real scene.