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most citedGeometric implicit neural representations for signed distance functions

10 citations · 13 across the 5 of their papers we have counts for

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cs.CV202510 cited

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…

cs.CV2025

Adaptive Training of INRs via Pruning and Densification

Diana Aldana, João Paulo Lima, Daniel Csillag +4

Encoding input coordinates with sinusoidal functions into multilayer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of low-dimensional signals,…

cs.CV2025

From Volume Rendering to 3D Gaussian Splatting: Theory and Applications

Vitor Pereira Matias, Daniel Perazzo, Vinicius Silva +4

The problem of 3D reconstruction from posed images is undergoing a fundamental transformation, driven by continuous advances in 3D Gaussian Splatting (3DGS). By modeling scenes exp…

cs.CV2025

FLOWING: Implicit Neural Flows for Structure-Preserving Morphing

Arthur Bizzi, Matias Grynberg, Vitor Matias +7

Morphing is a long-standing problem in vision and computer graphics, requiring a time-dependent warping for feature alignment and a blending for smooth interpolation. Recently, mul…

cs.CV20243 cited

Implicit Neural Representation of Tileable Material Textures

Hallison Paz, Tiago Novello, Luiz Velho

We explore sinusoidal neural networks to represent periodic tileable textures. Our approach leverages the Fourier series by initializing the first layer of a sinusoidal neural netw…