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.
@article{arxiv.2106.05187,
title = {Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields},
author = {Wang Yifan and Lukas Rahmann and Olga Sorkine-Hornung},
journal= {arXiv preprint arXiv:2106.05187},
year = {2022}
}
Comments
Accepted to ICLR 2022, 20 pages including appendix. Code available at https://github.com/yifita/idf