Energy minimisation-based multi-class multiinstance geometric primitives extraction from 3D point clouds

Abstract

Geometric primitives contained in three-dimensional (3D) point clouds can provide the meaningful and concise abstraction of 3D data, which plays a vital role in improving 3D vision-based intelligent applications. However, how to efficiently and robustly extract multiple geometric primitives from point clouds is still a challenge, especially when multiple instances of multiple classes of geometric primitives are present. In this study, a novel energy minimisation-based algorithm for multi-class multi-instance geometric primitives extraction from the 3D point cloud is proposed. First, an improved sampling strategy is proposed to generate model hypotheses. Then, an improved strategy to establish the neighbourhood is proposed to help construct and optimise an energy function for points labelling. After that, hypotheses and parameters of models are refined. Iterate this process until the energy does not decrease. Finally, models of multi-class multi-instance geometric primitives are simultaneously and robustly extracted from the 3D point cloud. In comparison with the state-of-the-art methods, it can automatically determine the classes and numbers of geometric primitives in the 3D point cloud. Experimental results with synthetic and real data validate the proposed algorithm.