KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberCRG-2017-3426
Permanent link to this recordhttp://hdl.handle.net/10754/666191
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AbstractIn this paper we propose an approach for computing multiple high-quality near-isometric dense correspondences between a pair of 3D shapes. Our method is fully automatic and does not rely on user-provided landmarks or descriptors. This allows us to analyze the full space of maps and extract multiple diverse and accurate solutions, rather than optimizing for a single optimal correspondence as done in most previous approaches. To achieve this, we propose a compact tree structure based on the spectral map representation for encoding and enumerating possible rough initializations, and a novel efficient approach for refining them to dense pointwise maps. This leads to a new method capable of both producing multiple high-quality correspondences across shapes and revealing the symmetry structure of a shape without a priori information. In addition, we demonstrate through extensive experiments that our method is robust and results in more accurate correspondences than state-of-the-art for shape matching and symmetry detection.
CitationRen, J., Melzi, S., Ovsjanikov, M., & Wonka, P. (2020). MapTree. ACM Transactions on Graphics, 39(6), 1–17. doi:10.1145/3414685.3417800
SponsorsThe authors would like to thank the anonymous reviewers for their valuable comments and helpful suggestions. Parts of this work were supported by the KAUST OSR Award No. CRG-2017-3426, and the ERC Starting Grant No. 758800 (EXPROTEA).
JournalACM Transactions on Graphics