Mesh Reduction to Exterior Surface Parts via Random Convex-Edge Affine Features
Data fusion of inputs from fundamentally different imaging techniques requires the identification of a common subset to allow for registration and alignment. In this paper, we describe how to reduce the isosurface of a volumetric object representation to its exterior surface, as this is the equivalent amount of data an optical surface scan of the very same specimen provides. Based on this, the alignment accuracy is improved, since only the overlap of both inputs has to be considered. Our approach allows for a rigorous reduction below 1 % of the original surface while preserving salient features and landmarks needed for further processing. The presented algorithm utilizes neighborhood queries from random points on an ellipsoid enclosing the specimen to identify data points in the mesh. Results for a real world object show a significant increase in alignment accuracy after reduction, compared to the alignment of the original representations via standard approaches.
KeywordsRoot Mean Square Error Convex Hull Iterative Close Point Exterior Surface Salient Region
This joint project is funded by the Deutsche Forschungsgemeinschaft (DFG), grant number BO 864/17-1, and by the Swiss National Science Foundation (SNF), grant number 200021L 141311. The Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) provides the optical scanning system as well as assistants to operate it. We thank our colleague Filip Sadlo for great help in improving the presentation of our work and implementing the reviewers comments. We also want to thank our project partners at the Swiss Federal Laboratories for Materials Science and Technology (Empa) for providing their expertise in metrology, the acquisition of numerous CT scans, and for having many fruitful discussions in frequent virtual or physical meetings. Above all, we thank Philipp Schütz, Urs Sennhauser, Jürgen Hofmann and Alexander Flisch.
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