Intrinsic Neural Fields: Learning Functions on Manifolds
Koestler L, Grittner D, Moeller M, Cremers D, Lahner Z (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13662 LNCS
Pages Range: 622-639
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Tel Aviv, ISR
ISBN: 9783031200854
DOI: 10.1007/978-3-031-20086-1_36
Abstract
Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative modeling. Some of their advantages are a sound theoretic foundation and an easy implementation in current deep learning frameworks. While neural fields have been applied to signals on manifolds, e.g., for texture reconstruction, their representation has been limited to extrinsically embedding the shape into Euclidean space. The extrinsic embedding ignores known intrinsic manifold properties and is inflexible wrt. Transfer of the learned function. To overcome these limitations, this work introduces intrinsic neural fields, a novel and versatile representation for neural fields on manifolds. Intrinsic neural fields combine the advantages of neural fields with the spectral properties of the Laplace-Beltrami operator. We show theoretically that intrinsic neural fields inherit many desirable properties of the extrinsic neural field framework but exhibit additional intrinsic qualities, like isometry invariance. In experiments, we show intrinsic neural fields can reconstruct high-fidelity textures from images with state-of-the-art quality and are robust to the discretization of the underlying manifold. We demonstrate the versatility of intrinsic neural fields by tackling various applications: texture transfer between deformed shapes & different shapes, texture reconstruction from real-world images with view dependence, and discretization-agnostic learning on meshes and point clouds.
Involved external institutions
Germany (DE)How to cite
APA:
Koestler, L., Grittner, D., Moeller, M., Cremers, D., & Lahner, Z. (2022). Intrinsic Neural Fields: Learning Functions on Manifolds. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 622-639). Tel Aviv, ISR: Springer Science and Business Media Deutschland GmbH.
MLA:
Koestler, Lukas, et al. "Intrinsic Neural Fields: Learning Functions on Manifolds." Proceedings of the 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, ISR Ed. Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner, Springer Science and Business Media Deutschland GmbH, 2022. 622-639.
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