Generalized unsupervised functional map learning for dense correspondence-Reference-Cited by-同舟云学术

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Generalized unsupervised functional map learning for dense correspondence

Published:2023-01-27 Issue:12 Volume:39 Page:6625-6638
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Han Li,Shi Xue,He Jinhai,Ma Huiwen,Dou Feng,Zhao Hongkai

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-022-02752-3.pdf

Reference44 articles.

1. Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. Comput. Graph. Forum. 28, 1383–1392 (2009)

2. Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: a quantum mechanical approach to shape analysis. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1626–1633. IEEE (2011)

3. Bronstein, M.M., Kokkinos, I.: Scale-invariant heat kernel signatures for non-rigid shape recognition. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1704–1711. IEEE (2010)

4. Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Proceedings of the European Conference on Computer Vision, pp. 356–369. Springer (2010)

5. Ovsjanikov, M., Ben-Chen, M., Solomon, J., Butscher, A., Guibas, L.: Functional maps: a flexible representation of maps between shapes. In: TOG (2012)

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