In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation matches intrinsic characteristics of point clouds: the number of points and their ordering within a cloud is not important as all points are drawn from the proximity of the object boundary. We postulate to represent each cloud as a parameterized probability distribution of points in space, which is defined by a generative neural network. The network operates by composing several spatial transformations of point locations. Once trained, it provides a natural framework for point cloud manipulation. For instance we can decouple cloud shape from its orientation and provide routines for aligning a new cloud into a default spatial orientation. To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector. We show that these embedding vectors capture semantic relationships between objects. Our method leverages generative invertible flow networks to learn embeddings as well as to generate point clouds. Thanks to this formulation and contrary to similar approaches, we are able to train our model in an end-to-end fashion. As a result, our model offers competitive or superior quantitative results on benchmark datasets, while enabling unprecedented capabilities to perform cloud manipulation tasks, such as point cloud registration and regeneration, by a generative network.

在本文中，我们提出了一种简单而有效的方法，将点云表示为从特定于云的概率分布中抽取的样本集。这种解释与点云的内在特征相匹配：点的数量及其在云中的排序并不重要，因为所有点都是从对象边界附近绘制的。我们假设将每个云表示为参数化的概率分布空间中的点，由生成神经网络定义。该网络通过组合点位置的多个空间变换来运行。一旦经过训练，它就为点云操作提供了一个自然的框架。例如，我们可以将云的形状与其方向分离，并提供将新云对齐到默认空间方向的例程。为了利用同一类对象之间的相似性并提高模型性能，我们转向权重共享：对属于同一族对象的点的密度进行建模的网络共享所有参数，但一个小的、特定于对象的嵌入向量除外。我们展示了这些嵌入向量捕获对象之间的语义关系。我们的方法利用生成可逆流网络来学习嵌入以及生成点云。多亏了这种公式，与类似的方法相反，我们能够以端到端的方式训练我们的模型。因此，我们的模型在基准数据集上提供了具有竞争力或卓越的定量结果，同时通过生成网络实现了执行云操作任务（例如点云注册和再生）的前所未有的能力。