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Author(s) Cleju, I.
Title Texture registration for 3D models
Abstract This thesis considers texture reconstruction for scanned 3D models. Given a geometric model and several photographs of the object, the texture is reconstructed in two steps: firstly, the images are registered (aligned) to the model, and, secondly, the texture is constructed from images. We split the first problem into initial registration, followed by optimization of the registration, and focus on the optimization part. We propose a framework which registers the images jointly, exploiting the model-image and image-image relations, using the mutual information criterion. The optimization uses a stochastic gradient-based algorithm, and its time complexity does not depend on the resolution of the data. We applied the framework to several models, and we achieved accuracy in the order of one pixel. We propose a novel evaluation method using epipolar geometry, and analyze three measures that allow comparison of texture registration with camera calibration data (weak calibration). The method is intended to detect biases of the texture registration. The proposed measures are well known in computer vision, and we investigated new aspects about them. We compared our texture registration algorithm with a state of the art camera calibration algorithm, and confirmed the high accuracy of our method. Finally, we developed a multi-band blending algorithm, based on the partition of unity over a mesh, to build a seamless texture.
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