Abstract
Recently, implicit neural representations (INRs) emerged as an effective method for reconstructing shapes. Several of such methods transform templates to target shapes. Current template-based methods lack proper regularization. In this work, we add a novel regularization to a deformable template approach and discuss the benefits of this regularization with a simple test case.
Original language | English |
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Number of pages | 4 |
Publication status | Published - 18 Nov 2022 |
Event | Geometric Deep Learning in Medical Image Analysis - Hotel CASA, Amsterdam, Netherlands Duration: 18 Dec 2021 → 18 Dec 2021 |
Conference
Conference | Geometric Deep Learning in Medical Image Analysis |
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Abbreviated title | GeoMedIA |
Country/Territory | Netherlands |
City | Amsterdam |
Period | 18/12/21 → 18/12/21 |
Keywords
- Latent space
- Shape reconstruction
- Implicit neural representation
- Neural ODE
Datasets
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Normal Form Autoencoder, data associated with the publication: ‘Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations’
Kalia, M. (Creator), Meijer, H. G. E. (Creator), Brunton, S. L. (Creator), Kutz, J. N. (Creator) & Brune, C. (Creator), 4TU.Centre for Research Data, 18 Jun 2021
Dataset