Samenvatting
We present co-skeletons, a new method that computes consistent curve skeletons for 3D shapes from a given family. We compute co-skeletons in terms of sampling density and semantic relevance, while preserving the desired characteristics of traditional, per-shape curve skeletonization approaches. We take the curve skeletons extracted by traditional approaches for all shapes from a family as input, and compute semantic correlation information of individual skeleton branches to guide an edge-pruning process via skeleton-based descriptors, clustering, and a voting algorithm. Our approach achieves more concise and family-consistent skeletons when compared to traditional per-shape methods. We show the utility of our method by using co-skeletons for shape segmentation and shape blending on real-world data.
Originele taal-2 | English |
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Pagina's (van-tot) | 62-72 |
Aantal pagina's | 11 |
Tijdschrift | Computers & Graphics |
Volume | 90 |
Vroegere onlinedatum | 16-mei-2020 |
DOI's | |
Status | Published - aug.-2020 |
Evenement | Symposium on Solid and Physical Modeling (SPM) collocated with the Shape Modeling International Conference (SMI) - Duur: 2-jun.-2020 → 4-jun.-2020 |