Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

Wang Yifan, Lukas Rahmann, Olga Sorkine-hornung

Keywords: implicit functions, shape reconstruction, shape representation, object reconstruction

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.

One-sentence Summary: We extend classic displacement mapping to the neural implicit framework. The resulting novel implicit representation demonstrates superior reconstruction accuracy, parameter efficiency and enable implicit shape editing such as detail transfer.