Continuous Depth Maps Merging Based3D Reconstruction

With the development of film and game industry, digital preservation of cultural heritage,3D printing technology, the virtual3D reconstruction of the scene and object becomes more and more fascinating. Multi-view stereo based3D reconstruction is a significant technique for those applications. Accuracy, robustness, efficiency are the three key issue to consider when design a3D reconstruction algorithm. Because of image distortion, image noise, repetitive textures, occlusion and other reasons, design a3D reconstruction algorithm that both achieve accurate3D model and robustness is usually a hard work, which restrict its application.In this dissertation, a series of algorithms are proposed to improve the accuracy and robustness of the image based3D reconstruction technique. On one hand, algorithm that can achieve accurate disparity and depth maps are designed, and depth merging algorithm is also proposed. On the other hand, continuous method is applied to improve the robustness of the3D reconstruction algorithm.More specifically, in this thesis, algorithms that associated with image distortion rectification, non-convex continuous based disparity estimation, convex continuous based method based disparity estimation are proposed. Our contribution includes:· We proposed a QR factorization based image radial distortion algorithm, improved the robustness of the radial parameter estimation of the multi-view3D reconstruction pipeline, which further improved the robustness of the3D reconstruction algorithm and the accuracy of the reconstructed3D model. The process is simplified by converting radial parameter estimation into matrix factorization.· We proposed a symmetric continuous depth map estimation algorithm, improved the quality of the depth map. Through model the stereo matching as a continuous MRF(Markov Random Field) problem, we built a symmetric functional for depth map estimation. In the data term of the functional, we applied both the color consistency and gradient consistency constraint. We used a multi-scale scheme for depth estimation. We also apply the left-right consistency soft constraint in the functional to further improve the depth map.· We proposed a convex optimization based depth estimation algorithm, improved the robustness of the algorithm and the accuracy of the depth map. We designed a functional for depth estimation with a hypothesis that the depth is piece-wise continuous, this assumption is more flexible than continuous assumption. We modeled the depth estimation problem as a free-discontinuity problem. We introduced the image segmentation prior into the functional to suppress the image noise. And we relaxed the proposed functional into a convex one, through which the estimated depth is independent of initial value, so it is more robust than the algorithms which depend on initial value.· We proposed a multiple continuous depth maps merging based3D reconstruction algorithm, improve the accuracy of the estimated3D model. We applied left-right depth consistency information to estimate a distance map, by which to control the speed of depth map update in different area of the image, in this way the quality of the depth maps can be further improved. We also designed a scheme to use the neighbor depth maps and images to optimize the depth map. When merging the depth maps, left-right consistency, normal of the point cloud and view direction of the camera were applied to denoise the depth maps.