A generative model for high-fidelity point clouds is of great importance in
synthesizing 3d environments for applications such as autonomous driving and
robotics. Despite the recent success of deep generative models for 2d images,
it is non-trivial to generate 3d point clouds without a comprehensive
understanding of both local and global geometric structures. In this paper, we
devise a new 3d point cloud generation framework using a divide-and-conquer
approach, where the whole generation process can be divided into a set of
patch-wise generation tasks. Specifically, all patch generators are based on
learnable priors, which aim to capture the information of geometry primitives.
We introduce point- and patch-wise transformers to enable the interactions
between points and patches. Therefore, the proposed divide-and-conquer approach
contributes to a new understanding of point cloud generation from the geometry
constitution of 3d shapes. Experimental results on a variety of object
categories from the most popular point cloud dataset, ShapeNet, show the
effectiveness of the proposed patch-wise point cloud generation, where it
clearly outperforms recent state-of-the-art methods for high-fidelity point
cloud generation.

基于分治法的点云生成框架，通过基于可学习先验的补丁生成器和点块转换器，实现了高保真度点云生成，并在 ShapeNet 数据集上优于最新的高保真度点云生成方法。