Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds

Vage Egiazarian, Savva Ignatyev, Alexey Artemov, Oleg Voynov, Andrey Kravchenko, Youyi Zheng, Luiz Velho, Evgeny Burnaev

2020

Abstract

Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design. Despite the recent progress in deep generative modelling, synthesis of finely detailed 3D surfaces, such as high-resolution point clouds, from scratch has not been achieved with existing learning-based approaches. In this work, we propose to employ the latent-space Laplacian pyramid representation within a hierarchical generative model for 3D point clouds. We combine the latent-space GAN and Laplacian GAN architectures proposed in the recent years to form a multi-scale model capable of generating 3D point clouds at increasing levels of detail. Our initial evaluation demonstrates that our model outperforms the existing generative models for 3D point clouds, emphasizing the need for an in-depth comparative study on the topic of multi-stage generative learning with point clouds.

Paper Citation

in Harvard Style

Egiazarian V., Ignatyev S., Artemov A., Voynov O., Kravchenko A., Zheng Y., Velho L. and Burnaev E. (2020). Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 421-428. DOI: 10.5220/0009102604210428

in Bibtex Style

@conference{visapp20,
author={Vage Egiazarian and Savva Ignatyev and Alexey Artemov and Oleg Voynov and Andrey Kravchenko and Youyi Zheng and Luiz Velho and Evgeny Burnaev},
title={Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={421-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009102604210428},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Latent-space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
SN - 978-989-758-402-2
AU - Egiazarian V.
AU - Ignatyev S.
AU - Artemov A.
AU - Voynov O.
AU - Kravchenko A.
AU - Zheng Y.
AU - Velho L.
AU - Burnaev E.
PY - 2020
SP - 421
EP - 428
DO - 10.5220/0009102604210428
PB - SciTePress