  Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only
played a minor role in the deep learning revolution. Leading methods for
learning generative models of shapes rely on implicit functions, and generate
meshes only after expensive iso-surfacing routines. To overcome these
challenges, we are inspired by a classical spatial data structure from computer
graphics, Binary Space Partitioning (BSP), to facilitate 3D learning. The core
ingredient of BSP is an operation for recursive subdivision of space to obtain
convex sets. By exploiting this property, we devise BSP-Net, a network that
learns to represent a 3D shape via convex decomposition. Importantly, BSP-Net
is unsupervised since no convex shape decompositions are needed for training.
The network is trained to reconstruct a shape using a set of convexes obtained
from a BSP-tree built on a set of planes. The convexes inferred by BSP-Net can
be easily extracted to form a polygon mesh, without any need for iso-surfacing.
The generated meshes are compact (i.e., low-poly) and well suited to represent
sharp geometry; they are guaranteed to be watertight and can be easily
parameterized. We also show that the reconstruction quality by BSP-Net is
competitive with state-of-the-art methods while using much fewer primitives.
Code is available at https://github.com/czq142857/BSP-NET-original.

Computer Vision and Pattern Recognition;Graphics;Machine Learning