26 citations · 53 across the 6 of their papers we have counts for
7 papers
M-plicits: Neural Implicit Surfaces via Nested Multiscale Residuals
Vinícius da Silva, Isabelle Melo, Matheus Bessa +8
Encoding input coordinates with sinusoidal functions into multi-layer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of surfaces defined as zero…
Geometric implicit neural representations for signed distance functions
Luiz Schirmer, Tiago Novello, Vinícius da Silva +5
\textit{Implicit neural representations} (INRs) have emerged as a promising framework for representing signals in low-dimensional spaces. This survey reviews the existing literatur…
High-Resolution Detection of Earth Structural Heterogeneities from Seismic Amplitudes using Convolutional Neural Networks with Attention layers
Luiz Schirmer, Guilherme Schardong, Vinícius da Silva +2
Earth structural heterogeneities have a remarkable role in the petroleum economy for both exploration and production projects. Automatic detection of detailed structural heterogene…
Multiresolution Neural Networks for Imaging
Hallison Paz, Tiago Novello, Vinicius Silva +5
We present MR-Net, a general architecture for multiresolution neural networks, and a framework for imaging applications based on this architecture. Our coordinate-based networks ar…
Neural Implicit Surface Evolution
Tiago Novello, Vinicius da Silva, Guilherme Schardong +3
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 represent…
Exploring Differential Geometry in Neural Implicits
Tiago Novello, Guilherme Schardong, Luiz Schirmer +3
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 t…
Neural Implicit Mapping via Nested Neighborhoods
Vinícius da Silva, Tiago Novello, Guilherme Schardong +3
We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (SDF) in real-time. We rely on nested neighborhoods of zero-level sets of neural…