2023
StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation
NeurIPS 2023poster
We present new insights and a novel paradigm for learning implicit neural representations (INR) of shapes. In particular, we shed light on the popular eikonal loss used for imposing a signed distance function constraint in INR. We show analytically that as the representation power of the network inc…