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Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
lib:3eb5754d8dc49618 (v1.0.0)
Authors: Vage Egiazarian,Savva Ignatyev,Alexey Artemov,Oleg Voynov,Andrey Kravchenko,Youyi Zheng,Luiz Velho,Evgeny Burnaev
ArXiv: 1912.06466
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1912.06466v1
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 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 recently proposed latent-space GAN and Laplacian GAN architectures to form a multi-scale model capable of generating 3D point clouds at increasing levels of detail. Our evaluation demonstrates that our model outperforms the existing generative models for 3D point clouds.
ArXiv: 1912.06466
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1912.06466v1
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 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 recently proposed latent-space GAN and Laplacian GAN architectures to form a multi-scale model capable of generating 3D point clouds at increasing levels of detail. Our evaluation demonstrates that our model outperforms the existing generative models for 3D point clouds.
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