Zobrazeno 1 - 10
of 737
pro vyhledávání: '"Velho Luiz"'
This work investigates the structure and representation capacity of $sinusoidal$ MLPs, which have recently shown promising results in encoding low-dimensional signals. This success can be attributed to its smoothness and high representation capacity.
Externí odkaz:
http://arxiv.org/abs/2407.21121
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 network with integer frequencies with a period $P$. We prove that the comp
Externí odkaz:
http://arxiv.org/abs/2402.02208
Autor:
Ptackova, Lenka, Velho, Luiz
Publikováno v:
Computer Aided Geometric Design 88 (2021) 102002
Discrete exterior calculus (DEC) offers a coordinate-free discretization of exterior calculus especially suited for computations on curved spaces. In this work, we present an extended version of DEC on surface meshes formed by general polygons that b
Externí odkaz:
http://arxiv.org/abs/2401.15436
Autor:
Schardong, Guilherme, Novello, Tiago, Paz, Hallison, Medvedev, Iurii, da Silva, Vinícius, Velho, Luiz, Gonçalves, Nuno
Face morphing is a problem in computer graphics with numerous artistic and forensic applications. It is challenging due to variations in pose, lighting, gender, and ethnicity. This task consists of a warping for feature alignment and a blending for a
Externí odkaz:
http://arxiv.org/abs/2308.13888
Autor:
Paz, Hallison, Novello, Tiago, Silva, Vinicius, Schirmer, Luiz, Schardong, Guilherme, Chagas, Fabio, Lopes, Helio, Velho, Luiz
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 are continuous both in space and in scale as they are composed of multip
Externí odkaz:
http://arxiv.org/abs/2208.11813
Autor:
Novello, Tiago, da Silva, Vinicius, Schardong, Guilherme, Schirmer, Luiz, Lopes, Helio, Velho, Luiz
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 neural implicit surfaces to the space-time $\mathbb{R}^3\time
Externí odkaz:
http://arxiv.org/abs/2201.09636
Autor:
Novello, Tiago, Schardong, Guilherme, Schirmer, Luiz, da Silva, Vinicius, Lopes, Helio, Velho, Luiz
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 level sets of neural implicit functions. To train a neural implicit
Externí odkaz:
http://arxiv.org/abs/2201.09263
Autor:
da Silva, Vinícius, Novello, Tiago, Schardong, Guilherme, Schirmer, Luiz, Lopes, Hélio, Velho, Luiz
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 SDFs, and mappings between them. This framework supports animations an
Externí odkaz:
http://arxiv.org/abs/2201.09147
Depth maps captured with commodity sensors often require super-resolution to be used in applications. In this work we study a super-resolution approach based on a variational problem statement with Tikhonov regularization where the regularizer is par
Externí odkaz:
http://arxiv.org/abs/2112.11085