Multiview Geometry for Texture Mapping 2D Images Onto 3D Range ...
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Multiview Geometry for Texture Mapping 2D Images Onto 3D Range ...
Multiview Geometry for Texture Mapping 2D Images Onto 3D Range Data. Computer .... recover the camera motion and the 3D
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assumption that the 3D scene contains a cluster of vertical and horizontal lines. This is a valid assumption in urban scene settings. The internal camera parameters consist of focal length, principal point, and other parameters in the camera calibration matrix [10]. They are derived from the scene’s vanishing points, whereby the 2D images are assumed to be free of distortion. Finally, the translation between and is computed after higher-order features such as 2D rectangles from the 2D image and 3D parallelepipeds from the 3D model are extracted and automatically matched. With this method, a few 2D images can be independently registered with the model . The algorithm will fail to produce satisfactory results in parts of the scene where there is a lack of 2D and 3D features for matching. Also, since each 2D image is independently registered with the 3D model, valuable information that can be extracted from relationships between the 2D images (SFM) is not utilized. In order to solve the aforementioned problems, an SFM module (Sec. 5) and final alignment module (Sec. 6) has been added into the system. These two modules increase the robustness of the reconstructed model, and improve the accuracy of the final texture mapping results. Therefore, the 2D-to-3D image-to-range registration algorithm is used in order to register a few 2D images (five shown in Fig. 1(a)) that produce results of high quality. The final registration of the 2D image sequence with the range model is performed after SFM is utilized (Sec. 5).
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% :B$? points of spondences between points of %E0 could be potentially used for theand computation of the similarity transformation between the two models. The set contains many outliers, due to the very simple closest-point algorithm utilized. However, can be further refined (Sec. 6.1) into a set of robust 3D point correspondences .
