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PCPNet: Learning Local Shape Properties from Raw Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2018)
In this paper, we propose PCPNET, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, ...
Deep Learning for Graphics
(The Eurographics Association, 2018)
In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In applications that operate on regular 2D domains, like image processing and computational photography, ...
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020)
Point clouds obtained with 3D scanners or by image‐based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local ...
Neurosymbolic Models for Computer Graphics
(The Eurographics Association and John Wiley & Sons Ltd., 2023)
Procedural models (i.e. symbolic programs that output visual data) are a historically-popular method for representing graphics content: vegetation, buildings, textures, etc. They offer many advantages: interpretable design ...