Phase Transitions, Distance Functions, and Implicit Neural Representations
Yaron Lipman 1 2
Abstract
Representing surfaces as zero level sets of neural
networks recently emerged as a powerful mod-
eling paradigm, named Implicit Neural Repre-
sentations (INRs), serving numerous downstream
applications in geometric deep learning and 3D vi-
sion. Training INRs previously required choosing
between occupancy and distance function repre-
...


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