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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, ...
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 ...
POP: Full Parametric model Estimation for Occluded People
(The Eurographics Association, 2019)
In the last decades, we have witnessed advances in both hardware and associated algorithms resulting in unprecedented access to volumes of 2D and, more recently, 3D data capturing human movement. We are no longer satisfied ...
Neural Semantic Surface Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2024)
We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Lack of annotated data prohibits direct inference of 3D semantic priors; ...