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6 September 2017 A multi-resolution texture fusion algorithm
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Texture fusion is essential for three-dimensional photo-realistic texture model reconstruction. Multi-resolution texture fusion mainly is applied to reconstruct three-dimensional models that the local texture is very realistic. This paper presents a multi-resolution texture fusion algorithm based on digital image processing. The technique utilizes the depth camera to obtain range data and the texture camera to obtain the local interested high-resolution texture of the object. And each point of the range images a pixel of the picture corresponding to this view should be found, so the map between high-resolution texture and local three-dimensional points could be obtained by calibrating texture camera and depth camera to register the local high-resolution texture with the low resolution texture collected by the depth camera or calibrating texture camera and depth camera. ICP is applied to register models, and mapping different-resolution textures onto three-dimensional models. Then the light source correction of textures is applied to remove the systematic differences such as lighting change which are achieved by computing linear regression correction mode. Finally, the global correction is applied to refine the observable variations in color that may still exist near the fusion boundaries. These corrections will utilize the grid triangle vertex color as a constraint to drive texture fusion to remove discontinuities from different resolutions. The advantage of this technique is that it utilizes the light source correction and global correction to fuse different resolutions textures. Experiments with this technique indicate that it significantly corrects the observable discontinuities within the overlapping areas, which are given from different resolutions, lighting change, non-lambertian object surface, etc.
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Yu Qin Li, Xiang Zhou, Jia Yu Guo, Tao Yang, and Zi Xuan Fei "A multi-resolution texture fusion algorithm ", Proc. SPIE 10410, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2017, 1041015 (6 September 2017);

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