Existing theories on 3D surface reconstruction impose strong constraints on feasible object shapes and often require error-free measurements. Moreover these theories can often only be applied to binary segmentations, i.e. the separation of an object from its background. We use the Delaunay complex and α-shapes to prove that topologically correct segmentations can be obtained under much more realistic conditions. Our key assumption is that sampling points represent object boundaries with a certain maximum error. We use this in the context of digitization, i.e. for the reconstruction based on supercover and m-cell intersection samplings. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Stelldinger, P. (2008). Topologically correct 3d surface reconstruction and segmentation from noisy samples. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4958 LNCS, pp. 274–285). Springer Verlag. https://doi.org/10.1007/978-3-540-78275-9_24
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