Extraction of Multi-class Multi-instance Geometric Primitives from Point Clouds Using Energy Minimization

Abstract

Point clouds play a vital role in self-driving vehicle, interactive media and other applications. However, how to efficiently and robustly extract multiple geometric primitives from point clouds is still a challenge. In this paper, a novel algorithm for extracting multiple instances of multiple classes of geometric primitives is proposed. First, a new sampling strategy is applied to generate model hypotheses. Next, an energy function is formulated from the view of point labelling. Then, an improved optimization technique is used to minimize the energy. After that, refine hypotheses and parameters. Iterate this process until the energy does not decrease. Finally, multi-class multi-instance of geometric primitives are correctly and robustly extracted. Different to existing methods, the type and number of models can be automatically determined. Experimental results validate the proposed algorithm.