NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

Eisenberger M, Novotny D, Kerchenbaum G, Labatut P, Neverova N, Cremers D, Vedaldi A (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

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

Publisher: IEEE Computer Society

Pages Range: 7469-7479

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

Event location: Virtual, Online, USA

ISBN: 9781665445092

DOI: 10.1109/CVPR46437.2021.00739

Abstract

We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i.e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them. The interpolation, expressed as a deformation field, changes the pose of the source shape to resemble the target, but leaves the object identity unchanged. NeuroMorph uses an elegant architecture combining graph convolutions with global feature pooling to extract local features. During training, the model is incentivized to create realistic deformations by approximating geodesics on the underlying shape space manifold. This strong geometric prior allows to train our model end-to-end and in a fully unsupervised manner without requiring any manual correspondence annotations. NeuroMorph works well for a large variety of input shapes, including non-isometric pairs from different object categories. It obtains state-of-the-art results for both shape correspondence and interpolation tasks, matching or surpassing the performance of recent unsupervised and supervised methods on multiple benchmarks.

Involved external institutions

Facebook, Inc. US United States (USA) (US) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Eisenberger, M., Novotny, D., Kerchenbaum, G., Labatut, P., Neverova, N., Cremers, D., & Vedaldi, A. (2021). NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 7469-7479). Virtual, Online, USA: IEEE Computer Society.

MLA:

Eisenberger, Marvin, et al. "NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go." Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, Online, USA IEEE Computer Society, 2021. 7469-7479.

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