Neural Feature Matching in Implicit 3D Representations

Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Recently, neural implicit functions have achieved impressive results for encoding 3D shapes. Conditioning on low-dimensional latent codes generalises a single implicit function to learn shared representation space for a variety of shapes, with the advantage of smooth interpolation. While the benefits from the global latent space do not correspond to explicit points at local level, we propose to track the continuous point trajectory by matching implicit features with the latent code interpolating between shapes, from which we corroborate the hierarchical functionality of the deep implicit functions, where early layers map the latent code to fitting the coarse shape structure, and deeper layers further refine the shape details. Furthermore, the structured representation space of implicit functions enables to apply feature matching for shape deformation, with the benefits to handle topology and semantics inconsistency, such as from an armchair to a chair with no arms, without explicit flow functions or manual annotations.
Original languageEnglish
Title of host publicationProceedings of the 38th International Conference on Machine Learning
PublisherPMLR
Pages1582-1593
Publication statusE-pub ahead of print - 18 Jul 2021
EventThirty-eighth International Conference on Machine Learning - Online
Duration: 18 Jul 202124 Jul 2021
https://icml.cc/

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume139
ISSN (Electronic)2640-3498

Conference

ConferenceThirty-eighth International Conference on Machine Learning
Abbreviated titleICML 2021
Period18/07/2124/07/21
Internet address

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