Learning Representations and Generative Models for 3D Point Clouds
Learning Representations and Generative Models for 3D Point Clouds
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发表时间：
2017-07
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通讯作者：
Panos Achlioptas;Olga Diamanti;Ioannis Mitliagkas;L. Guibas
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作者：
Panos Achlioptas;Olga Diamanti;Ioannis Mitliagkas;L. Guibas
三维几何数据为研究表示学习和生成建模提供了一个很好的领域。在本文中，我们将几何数据表示为点云。我们介绍了一种具有最先进的重建质量和泛化能力的深度自动编码器（AE）网络。学习到的表示在3D识别任务上优于现有的方法，并且可以通过简单的代数操作进行形状编辑，例如语义部分编辑，形状类比和形状插值，以及形状补全。我们对不同的生成模型进行了深入的研究，包括在原始点云上运行的gan，在我们的AEs的固定潜在空间中训练的显著改进的gan，以及高斯混合模型（GMMs）。为了定量评估生成模型，我们引入了基于点云集之间匹配的样本保真度和多样性度量。有趣的是，我们对泛化、保真度和多样性的评估表明，在我们的AEs的潜在空间中训练的gmm总体上产生了最好的结果。
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 AutoEncoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation, as well as shape completion. We perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs, and Gaussian Mixture Models (GMMs). To quantitatively evaluate generative models we introduce measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs yield the best results overall.