  This paper proposes BPNet, a novel end-to-end deep learning framework to
learn B\'ezier primitive segmentation on 3D point clouds. The existing works
treat different primitive types separately, thus limiting them to finite shape
categories. To address this issue, we seek a generalized primitive segmentation
on point clouds. Taking inspiration from B\'ezier decomposition on NURBS
models, we transfer it to guide point cloud segmentation casting off primitive
types. A joint optimization framework is proposed to learn B\'ezier primitive
segmentation and geometric fitting simultaneously on a cascaded architecture.
Specifically, we introduce a soft voting regularizer to improve primitive
segmentation and propose an auto-weight embedding module to cluster point
features, making the network more robust and generic. We also introduce a
reconstruction module where we successfully process multiple CAD models with
different primitives simultaneously. We conducted extensive experiments on the
synthetic ABC dataset and real-scan datasets to validate and compare our
approach with different baseline methods. Experiments show superior performance
over previous work in terms of segmentation, with a substantially faster
inference speed.
