Smooth shells: Multi-scale shape registration with functional maps

Eisenberger M, Lähner Z, Cremers D (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Pages Range: 12262-12271

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: Virtual, Online, USA

DOI: 10.1109/CVPR42600.2020.01228

Abstract

We propose a novel 3D shape correspondence method based on the iterative alignment of so-called smooth shells. Smooth shells define a series of coarse-to-fine shape approximations designed to work well with multiscale algorithms. The main idea is to first align rough approximations of the geometry and then add more and more details to refine the correspondence. We fuse classical shape registration with Functional Maps by embedding the input shapes into an intrinsic-extrinsic product space. Moreover, we disambiguate intrinsic symmetries by applying a surrogate based Markov chain Monte Carlo initialization. Our method naturally handles various types of noise that commonly occur in real scans, like non-isometry or incompatible meshing. Finally, we demonstrate state-of-the-art quantitative results on several datasets and show that our pipeline produces smoother, more realistic results than other automatic matching methods in real world applications.

Involved external institutions

Technische Universität München (TUM)

Germany (DE)

How to cite

APA:

Eisenberger, M., Lähner, Z., & Cremers, D. (2020). Smooth shells: Multi-scale shape registration with functional maps. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 12262-12271). Virtual, Online, USA: IEEE Computer Society.

MLA:

Eisenberger, Marvin, Zorah Lähner, and Daniel Cremers. "Smooth shells: Multi-scale shape registration with functional maps." Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Virtual, Online, USA IEEE Computer Society, 2020. 12262-12271.

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