DeepSDF: Learning Continuous Signed Distance Functions for ...
DeepSDF: Learning Continuous Signed Distance Functionsfor Shape RepresentationJeong Joon Park1,3Peter Florence2,3Julian Straub3Richard Newcombe3Steven Lovegrove31University of Washington2Massachusetts Institute of Technology3Facebook Reality LabsFigure 1:DeepSDF represents Signed Distance Functions (SDFs) of shapes via latent code-conditioned feed-forward decoder images are raycast renderings of DeepSDF interpolating between two shapes in the learned shape latent space. Best viewed graphics, 3D computer vision and roboticscommunities have produced multiple approaches to rep-resenting 3D geometry for rendering and provide trade-offs across fidelity, efficiency and com-pression capabilities.
tion is a continuous function that, for a given spatial point, outputs the point’s distance to the closest surface, whose sign encodes whether the point is inside (negative) or out-side (positive) of the watertight surface: SDF(x) = s : x∈ R3, s ∈ R. (1) The underlying surface is implicitly represented by the iso-surface of SDF(·) = 0.
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