Abstract

In this paper, we introduce a non-associative higher-order graphical model to tackle the problem of semantic labeling of 3D point clouds. For this task, existing higher-order models overlook the relation- ships between the different classes and simply encourage the nodes in the cliques to have consistent labelings. We address this issue by devising a set of non-associative context patterns that describe higher-order geometric relationships between different class labels within the cliques. To this end, we propose a method to extract informative cliques in 3D point clouds that provide more knowledge about the context of the scene. We evaluate our approach on three challenging outdoor point cloud datasets. Our exper- iments evidence the benefits of our non-associative higher-order Markov networks over state-of-the-art point cloud labeling techniques.