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Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
[article]
2019
arXiv
pre-print
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
arXiv:1912.06466v1
fatcat:mqgtoxcfsradxmn723t52johti