Transcription of DeepSDF: Learning Continuous Signed Distance Functions for ...
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DeepSDF: Learning Continuous Signed Distance Functions for ...
DeepSDF: Learning Continuous Signed Distance Functionsfor Shape RepresentationJeong Joon Park1,3 Peter Florence2,3 Julian Straub3 Richard Newcombe3 Steven Lovegrove31 University of Washington2 Massachusetts Institute of Technology3 Facebook 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. In this work, we introduce DeepSDF,a learned Continuous Signed Distance Function (SDF) rep-resentation of a class of shapes that enables high qual-ity shape representation, interpolation and completion frompartial and noisy 3D input data.
and application of this formulation to shape modeling and completion. Our models produce high quality continuous surfaces with complex topologies, and obtain state-of-the-art results in quantitative comparisons for shape reconstruc-tion and completion. As an example of the effectiveness of our method, our models use only 7.4 MB (megabytes)
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