论文标题
生成点云的超网络方法
Hypernetwork approach to generating point clouds
论文作者
论文摘要
在这项工作中，我们提出了一种新的方法，用于生成利用超网络属性的3D点云。与仅学习3D对象表示的现有方法相反，我们的方法同时找到了对象及其3D表面的表示。我们的超云方法的主要思想是建立一个超级网络，该网络返回经过训练的特定神经网络（目标网络）的权重，该网络从均匀的单位球分布到3D形状。结果，可以使用从假定的先验分布中的点采样来生成特定的3D形状，并使用目标网络转换采样点。由于超网络基于经过训练以重建现实3D形状的自动编码器体系结构，因此目标网络权重可以视为3D形状表面的参数化，而不是通常由竞争方法返回的点云的标准表示。所提出的体系结构允许以生成方式找到基于网格的3D对象的表示，同时提供质量的点云与最新方法。
In this work, we propose a novel method for generating 3D point clouds that leverage properties of hyper networks. Contrary to the existing methods that learn only the representation of a 3D object, our approach simultaneously finds a representation of the object and its 3D surface. The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape. As a consequence, a particular 3D shape can be generated using point-by-point sampling from the assumed prior distribution and transforming sampled points with the target network. Since the hyper network is based on an auto-encoder architecture trained to reconstruct realistic 3D shapes, the target network weights can be considered a parametrization of the surface of a 3D shape, and not a standard representation of point cloud usually returned by competitive approaches. The proposed architecture allows finding mesh-based representation of 3D objects in a generative manner while providing point clouds en pair in quality with the state-of-the-art methods.
