The Research Of View Synthesis Based On Depth Image

Realistic scene generation is the core of virtual reality research. From existing pieces of discrete viewpoint image synthesis a new image, in accordance with the needs of the application of virtual reality. The view synthesis technology study how to generate a new point of view of the scene under the corresponding image, with the use of two or multiple images from a same scene.The research of depth image-based view synthesis includes the estimation of depth image and view synthesis algorithm. Among them, this study related to the use of computer vision algorithm to obtain the image depth information. The main contents include camera calibration, fundamental matrix estimation, stereo matching and depth estimation and so on. Firstly, this paper studies the current method of camera calibration and an improved fast camera calibration method based on LCD monitor was proposed. Secondly, stereo matching based on feature of the corner points was used to obtain a sparse matching, Robust Fundamental Matrix F was estimated by using the improved M-Estimators, and then dual-threshold-based phases stereo matching was used to obtain dense matching. Finally, the three-dimensional information of the corresponding point was calculated by matching point.Depth image-based view synthesis is the focus of this study. A depth image view synthesis algorithm based on Inverse Mapping was presented, which was based on the continuity of the depth on the surface, using the overall matching and order holding of the polar line, as well as the boundary point of information based on generalized disparity hided in the depth image. The depth information of corresponding pixel in new image was obtained through a primary reference source, and then the gray information of corresponding pixel in new image was obtained in the reference image by the reverse mapping algorithm, eventually a new image was obtained. As the occlusion relations and projection of regional expansion in the new view, empty inevitably exist. With the knowledge of polar line, an improved algorithm based on reverse mapping of single point was presented to fill the blank space in the new image. The blank space in the new image was filled, and a complete new image was obtained, with the use of little reference source image.