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Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields

Wang Yifan · Lukas Rahmann · Olga Sorkine-hornung

Keywords: [ implicit functions ]


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 transferand demonstrate superior representational power, training stability, and generalizability.

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