  This paper presents the methods that have participated in the SHREC 2022
track on the fitting and recognition of simple geometric primitives on point
clouds. As simple primitives we mean the classical surface primitives derived
from constructive solid geometry, i.e., planes, spheres, cylinders, cones and
tori. The aim of the track is to evaluate the quality of automatic algorithms
for fitting and recognising geometric primitives on point clouds. Specifically,
the goal is to identify, for each point cloud, its primitive type and some
geometric descriptors. For this purpose, we created a synthetic dataset,
divided into a training set and a test set, containing segments perturbed with
different kinds of point cloud artifacts. Among the six participants to this
track, two are based on direct methods, while four are either fully based on
deep learning or combine direct and neural approaches. The performance of the
methods is evaluated using various classification and approximation measures.

Graphics;Numerical Analysis;Numerical Analysis