Non-associative Higher-Order Markov Networks for Point Cloud Classification

Mohammad Najafi, Sarah Taghavi Namin, Mathieu Salzmann, and Lars Petersson

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 relationships 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 experiments evidence the benefits of our non-associative higher-order Markov networks over state-of-the-art point cloud labeling techniques.

Keywords: Non-associative Markov networks, Higher-order graphical models, 3D point clouds, Semantic labeling

LNCS 8693, p. 500 ff.

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