Representing complex 3D objects as simple geometric primitives, known as
shape abstraction, is important for geometric modeling, structural analysis,
and shape synthesis. In this paper, we propose an unsupervised shape
abstraction method to map a point cloud into a compact cuboid representation.
We jointly predict cuboid allocation as part segmentation and cuboid shapes and
enforce the consistency between the segmentation and shape abstraction for
self-learning. For the cuboid abstraction task, we transform the input point
cloud into a set of parametric cuboids using a variational auto-encoder
network. The segmentation network allocates each point into a cuboid
considering the point-cuboid affinity. Without manual annotations of parts in
point clouds, we design four novel losses to jointly supervise the two branches
in terms of geometric similarity and cuboid compactness. We evaluate our method
on multiple shape collections and demonstrate its superiority over existing
shape abstraction methods. Moreover, based on our network architecture and
learned representations, our approach supports various applications including
structured shape generation, shape interpolation, and structural shape
clustering.

本文提出了一种无监督形状抽象方法，将点云转换为紧凑的长方体表示，并以分割和长方体形状的联合预测为抽象任务，以自学习的方式加强分割和形状抽象之间的一致性，该方法不需要手动注释点云中的零件。本文通过几种形状集的评估，证明了该方法优于现有的形状抽象方法，支持结构形状生成、形状插值和结构形状聚类等各种应用。