We introduce a neural implicit framework that exploits the differentiable properties of neural
networks and the discrete geometry of point-sampled surfaces to approximate them as the …

Implicit neural representations (INRs) have emerged as a promising framework for
representing signals in low-dimensional spaces. This survey reviews the existing literature …

This work investigates the use of smooth neural networks for modeling dynamic variations of
implicit surfaces under the level set equation (LSE). For this, it extends the representation of …

Multiresolution Neural Networks for Multiscale Signal Representation Page 1
Multiresolution Neural Networks for Multiscale Signal Representation Luiz Velho, Hallison …

Abstract Implicit Neural Representations has emerged as a promising framework for
representing different types of signals in low dimensions. This survey focuses on the existing …