| Our research is focused on the problems of the recovery of a realistic textured model from image sequences and some critical issues related to this subject, such as dense matching to stereo images. The thesis investigates both the theoretical and practical feasibility in recovering the complete structure of an object from a long image sequence captured around it with occlusions. In this case, some points may be visible in a number of frames and then disappear in the following several frames. The main contributions of the thesis are as follows:1. We propose a new two level matching algorithm for seed points in propagation. Firstly, our algorithm compares edge similarity around the target pixel based on edge extraction. This level of feature matching is both simple and reliable. Then intensity similarity is compared within a small search window, which is constrained by the results of the first level matching. In this way, the corresponding point is located accurately. This algorithm efficiently avoids mismatches caused by the repetitive patterns.2. A novel and efficient dense matching method is proposed, which is based on the propagation by the Voronoi decomposition of the images. The significant merit of the algorithm is that it can be applied to a wide range of image pairs including those with large disparities, with or without rectification. And it may involve both textured part and less textured part of the images. Our dense matching begins from a number of seed points, which are reliably matched by feature tracking. Then corresponding relations are propagated from all of the seeds respectively. The decomposition of the images into Voronoi diagram restricts bad propagations within a single cell. It improves the performance of dense matching both in efficiency and accuracy.3. A novel 3D reconstruction algorithm with missing data is presented, by which the complete structure of the target can be recovered. Firstly, images taken around the target are divided into several subsets. Each subset has common feature points. Secondly, Euclidean reconstruction is performed by iterative factorization with all of these points visible in each image of a certain subset. Then results coming from different subset are brought into a common coordinate frame by similarity transformations. Finally, global optimization is applied to minimize the back projection errors, which can refine the data and produce a jointly optimal 3Dstructure. A significant merit of the algorithm is that it can deal with occlusions and a complete 3D model is recovered from the long image sequence.4. A new global optimization algorithm with missing data is proposed. To remedy the drawback of cutting a long image sequence into several subsets in our 3D reconstruction algorithm, global optimization with a weighting matrix is applied to refine the results, in which the visible and missing data are arranged together. The back projection error is minimized over the estimated camera matrices and 3D points. In our optimization, the visible points and the missing data are treated uniformly by adding different weights. Experiments demonstrate that the algorithmis both effective and accurate.5. The 3D reconstruction algorithm with constrained triangulation has been tested on both simulate data and real images with satisfactory results. The long image sequence is taken from 360 degrees around the target. Each point is visible in about 10 consecutive images and occluded in the rest of the images. The complete structure of the building is recovered with realistic textures and we also generate an augmented scene to demonstrate the good performance of our algorithm. The structures recovered in this way have better visualization effect than that of the virtual scenes.Future researches on this topic include: go on the work with missing data to further improve its accuracy and feasibility; decrease human interactions in the computation; improve the robustness of self-calibration by prior knowledge of orthogonal / parallel lines and ort...
|
PDF Full Text Request