Abstract
Geometric deep learning is a promising approach to bring the representational power of deep neural networks to 3D data. Explicit 3D representations such as point clouds or meshes can have varying and often a huge number of dimensions, what limits their use as an input to a neural network. Implicit representations such as signed distance functions (SDF) are on the contrary low-dimensional and fixed representations of the structure of a 3D shape that can be easily fed into a neural network. In this paper, we demonstrate how deep SDF neural networks can be used to precisely predict the deformation of a material after the application of a specific force. The model is trained using a set of custom finite element simulations in order to generalize to unseen forces.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Feng, Y., Feng, Y., You, H., Zhao, X., Gao, Y.: Meshnet: mesh neural network for 3d shape representation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8279–8286 (2019)
Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph Comput. Graph. 21(4), 163–169 (1987)
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 165–174 (2019)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. arXiv:1612.00593 [cs] (2016)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. arXiv:1706.02413 [cs] (2017)
Ranjan, A., Bolkart, T., Sanyal, S., Black, M.J.: Generating 3D faces using convolutional mesh autoencoders. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 704–720 (2018)
Acknowledgement
This work has been funded by the Federal Ministry of Education and Research (BMBF) - ML@Karoprod (01IS18055) and the German Research Foundation (DFG, 416228727) - SFB 1410 Hybrid Societies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Farahani, A., Vitay, J., Hamker, F.H. (2022). Deep Neural Networks for Geometric Shape Deformation. In: Bergmann, R., Malburg, L., Rodermund, S.C., Timm, I.J. (eds) KI 2022: Advances in Artificial Intelligence. KI 2022. Lecture Notes in Computer Science(), vol 13404. Springer, Cham. https://doi.org/10.1007/978-3-031-15791-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-15791-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15790-5
Online ISBN: 978-3-031-15791-2
eBook Packages: Computer ScienceComputer Science (R0)