BriefGPT.xyz
Jul, 2017
学习3D点云的表示和生成模型
Representation Learning and Adversarial Generation of 3D Point Clouds
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Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas
TL;DR
该论文探讨了点云作为表示几何数据的方法，利用深度自编码器网络来提高3D识别及形状编辑的表现，并对不同的生成模型进行了研究，发现在AE的潜在空间中训练的高斯混合模型具有最佳的生成效果。
Abstract
Three-dimensional geometric data offer an excellent domain for studying representation learning and
generative modeling
. In this paper, we look at geometric data represented as
point clouds
. We introduce a deep <
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