Presentation
Learning Smooth Neural Functions via Lipschitz Regularization
SessionTechnical Papers
Event Type
Technical Paper
Research & Education
Virtual
Full Conference Supporter
Full Conference
Virtual Conference Supporter
Virtual Conference
Exhibitor Additional Full Conference
Exhibitor Full Conference
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DescriptionSmoothness is an essential property in geometry processing with neural fields. We introduce a Lipschitz regularization to encourage smoothness in functions parameterized by neural networks. Ours is computationally fast and can be implemented in four lines of code. We demonstrate the effectiveness on shape interpolation, extrapolation, and latent code optimization.
