Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Sellán, Silvia"'
Resampling from a target measure whose density is unknown is a fundamental problem in mathematical statistics and machine learning. A setting that dominates the machine learning literature consists of learning a map from an easy-to-sample prior, such
Externí odkaz:
http://arxiv.org/abs/2410.07003
Autor:
Bhargava, Manas, Schreck, Camille, Freire, Marco, Hugron, Pierre-Alexandre, Lefebvre, Sylvain, Sellán, Silvia, Bickel, Bernd
We present a computational approach for unfolding 3D shapes isometrically into the plane as a single patch without overlapping triangles. This is a hard, sometimes impossible, problem, which existing methods are forced to soften by allowing for map d
Externí odkaz:
http://arxiv.org/abs/2408.06944
Autor:
Sellán, Silvia, Jacobson, Alec
Reconstructing a surface from a point cloud is an underdetermined problem. We use a neural network to study and quantify this reconstruction uncertainty under a Poisson smoothness prior. Our algorithm addresses the main limitations of existing work a
Externí odkaz:
http://arxiv.org/abs/2309.11993
Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties. Current methods to quantify them are either heuristic or computationally dema
Externí odkaz:
http://arxiv.org/abs/2309.03185
Signed distance fields (SDFs) are a widely used implicit surface representation, with broad applications in computer graphics, computer vision, and applied mathematics. To reconstruct an explicit triangle mesh surface corresponding to an SDF, traditi
Externí odkaz:
http://arxiv.org/abs/2308.09813
We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fracture simulation is powered by a recent physically based algorithm that
Externí odkaz:
http://arxiv.org/abs/2210.11463
Autor:
Sellán, Silvia, Jacobson, Alec
We introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm for recovering shapes from 3D point clouds. Instead of outputting an implicit function, we represent the reconstructed shape as a modified Gaussian Process,
Externí odkaz:
http://arxiv.org/abs/2206.15236
We survey the treatment of sex and gender in the Computer Graphics research literature from an algorithmic fairness perspective. The established practices on the use of gender and sex in our community are scientifically incorrect and constitute a for
Externí odkaz:
http://arxiv.org/abs/2206.00480
Autor:
Sellán, Silvia, Luong, Jack, Da Silva, Leticia Mattos, Ramakrishnan, Aravind, Yang, Yuchuan, Jacobson, Alec
Drawing a direct analogy with the well-studied vibration or elastic modes, we introduce an object's fracture modes, which constitute its preferred or most natural ways of breaking. We formulate a sparsified eigenvalue problem, which we solve iterativ
Externí odkaz:
http://arxiv.org/abs/2111.05249
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