Non-rigid 3D shape retrieval via large margin nearest neighbor embedding
Chiotellis I, Triebel R, Windheuser T, Cremers D (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9906 LNCS
Pages Range: 327-342
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Amsterdam, NLD
ISBN: 9783319464749
DOI: 10.1007/978-3-319-46475-6_21
Abstract
In this paper, we propose a highly efficient metric learning approach to non-rigid 3D shape analysis. From a training set of 3D shapes from different classes, we learn a transformation of the shapes which optimally enforces a clustering of shapes from the same class. In contrast to existing approaches, we do not perform a transformation of individual local point descriptors, but a linear embedding of the entire distribution of shape descriptors. It turns out that this embedding of the input shapes is sufficiently powerful to enable state of the art retrieval performance using a simple nearest neighbor classifier. We demonstrate experimentally that our approach substantially outperforms the state of the art non-rigid 3D shape retrieval methods on the recent benchmark data set SHREC’14 Non-Rigid 3D Human Models, both in classification accuracy and runtime.
Involved external institutions
Germany (DE)How to cite
APA:
Chiotellis, I., Triebel, R., Windheuser, T., & Cremers, D. (2016). Non-rigid 3D shape retrieval via large margin nearest neighbor embedding. In Bastian Leibe, Nicu Sebe, Max Welling, Jiri Matas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 327-342). Amsterdam, NLD: Springer Verlag.
MLA:
Chiotellis, Ioannis, et al. "Non-rigid 3D shape retrieval via large margin nearest neighbor embedding." Proceedings of the 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, NLD Ed. Bastian Leibe, Nicu Sebe, Max Welling, Jiri Matas, Springer Verlag, 2016. 327-342.
BibTeX: Download