  We introduce CAPRI-Net, a neural network for learning compact and
interpretable implicit representations of 3D computer-aided design (CAD)
models, in the form of adaptive primitive assemblies. Our network takes an
input 3D shape that can be provided as a point cloud or voxel grids, and
reconstructs it by a compact assembly of quadric surface primitives via
constructive solid geometry (CSG) operations. The network is self-supervised
with a reconstruction loss, leading to faithful 3D reconstructions with sharp
edges and plausible CSG trees, without any ground-truth shape assemblies. While
the parametric nature of CAD models does make them more predictable locally, at
the shape level, there is a great deal of structural and topological
variations, which present a significant generalizability challenge to
state-of-the-art neural models for 3D shapes. Our network addresses this
challenge by adaptive training with respect to each test shape, with which we
fine-tune the network that was pre-trained on a model collection. We evaluate
our learning framework on both ShapeNet and ABC, the largest and most diverse
CAD dataset to date, in terms of reconstruction quality, shape edges,
compactness, and interpretability, to demonstrate superiority over current
alternatives suitable for neural CAD reconstruction.
