Abstract
Fitting geometric primitives to 3D point cloud data
bridges a gap between low-level digitized 3D data and highlevel structural information on the underlying 3D shapes.
As such, it enables many downstream applications in 3D
data processing. For a long time, RANSAC-based methods
have been the gold standard for such primitive fitting problems, but they require careful per-input parameter tuning
and thus do not scale well for large datasets with diverse
shapes. In this work, we introduce Supervised Primitive
Fitting Network (SPFN), an end-to-end neural network that
can robustly detect a varying number of primitives at different scales without any user control. The network is supervised using ground truth primitive surfaces and primitive
membership for the input points. Instead of directly predicting the primitives, our architecture first predicts per-point
properties and then uses a differential model estimation
module to compute the primitive type and parameters. We
evaluate our approach on a novel benchmark of ANSI 3D
mechanical component models and demonstrate a signifi-
cant improvement over both the state-of-the-art RANSACbased methods and the direct neural prediction.