Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
Authors: Wang Yifan,Lukas Rahmann,Olga Sorkine-hornung
ICLR 2022,Poster

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.