  Plane detection in 3D point clouds is a crucial pre-processing step for
applications such as point cloud segmentation, semantic mapping and SLAM. In
contrast to many recent plane detection methods that are only applicable on
organized point clouds, our work is targeted to unorganized point clouds that
do not permit a 2D parametrization. We compare three methods for detecting
planes in point clouds efficiently. One is a novel method proposed in this
paper that generates plane hypotheses by sampling from a set of points with
estimated normals. We named this method Oriented Point Sampling (OPS) to
contrast with more conventional techniques that require the sampling of three
unoriented points to generate plane hypotheses. We also implemented an
efficient plane detection method based on local sampling of three unoriented
points and compared it with OPS and the 3D-KHT algorithm, which is based on
octrees, on the detection of planes on 10,000 point clouds from the SUN RGB-D
dataset.
