Papers › Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
Wang Yifan, Lukas Rahmann, Olga Sorkine-Hornung
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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