A New Fusion Algorithm for Depth Images Based on Virtual Views

Common depth image fusion methods use each original image as a reference plane and fuse the depth images using mutual projection. These methods can eliminate inconsistency between the depth images, but they cannot alleviate the point cloud redundancy and computational complexity. This article proposes a virtual view method for depth image fusion, defines a limited number of virtual views by means of view clustering, reduces the redundant calculations, and covers all scenes as much as possible. The depth image is merged ray by ray, and a reliable depth value is obtained via the F-test. Compared with the modified semiglobal matching (TSGM) stereo dense matching algorithm, the accuracy is improved by approximately 50% and the roughness is improved by approximately 50%. Compared with the classic surface reconstruction (SURE) fusion algorithm, there is more fusion depth value in each ray, and the accuracy and roughness are slightly improved. In addition, the algorithm of this article greatly reduces the number of reference planes.