26 June 2017 3D registration of depth data of porous surface coatings based on 3D phase correlation and the trimmed ICP algorithm
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A critical factor of endoprostheses is the quality of the tribological pairing. The objective of this research project is to manufacture stochastically porous aluminum oxide surface coatings with high wear resistance and an active friction minimization. There are many experimental and computational techniques from mercury porosimetry to imaging methods for studying porous materials, however, the characterization of disordered pore networks is still a great challenge. To meet this challenge it is striven to gain a three dimensional high resolution reconstruction of the surface. In this work, the reconstruction is approached by repeatedly milling down the surface by a fixed decrement while measuring each layer using a confocal laser scanning microscope (CLSM). The so acquired depth data of the successive layers is then registered pairwise. Within this work a direct registration approach is deployed and implemented in two steps, a coarse and a fine alignment. The coarse alignment of the depth data is limited to a translational shift which occurs in horizontal direction due to placing the sample in turns under the CLSM and the milling machine and in vertical direction due to the milling process itself. The shift is determined by an approach utilizing 3D phase correlation. The fine alignment is implemented by the Trimmed Iterative Closest Point algorithm, matching the most likely common pixels roughly specified by an estimated overlap rate. With the presented two-step approach a proper 3D registration of the successive depth data of the layer is obtained.
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Nina Loftfield, Nina Loftfield, Markus Kästner, Markus Kästner, Eduard Reithmeier, Eduard Reithmeier, } "3D registration of depth data of porous surface coatings based on 3D phase correlation and the trimmed ICP algorithm", Proc. SPIE 10333, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials III, 1033310 (26 June 2017); doi: 10.1117/12.2269781; https://doi.org/10.1117/12.2269781


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