Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

Wang Yifan, Lukas Rahmann, Olga Sorkine-Hornung

6/9/2021

Keywords: Fundamentals, Data-Driven Method, Coarse-to-Fine, Hybrid Geometry Representation

Venue: IJCAI 2021

Bibtex: @inproceedings{yifan2021idf, publisher = {International Joint Conferences on Artificial Intelligence Organization}, booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI)}, author = {Wang Yifan and Lukas Rahmann and Olga Sorkine-Hornung}, title = {Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields}, year = {2021}, url = {http://arxiv.org/abs/2106.05187v2}, entrytype = {inproceedings}, id = {yifan2021idf} }

Abstract

We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method represents a complex surface as a smooth base surface plus a displacement along the base's normal directions, resulting in a frequency-based shape decomposition, where the high frequency signal is constrained geometrically by the low frequency signal. Importantly, this disentanglement is unsupervised thanks to a tailored architectural design that has an innate frequency hierarchy by construction. We explore implicit displacement field surface reconstruction and detail transfer and demonstrate superior representational power, training stability and generalizability.

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