Comparison of belief propagation and graph-cut approaches for contextual classification of 3D lidar point cloud data

Loic Landrieu (1) , Clément Mallet (1) , Martin Weinmann (2)

1 MATIS - Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution
2 KIT - Karlsruhe Institute of Technology

In this paper, we focus on the classification of lidar point cloud data acquired via mobile laser scanning, whereby the classification relies on a context model based on a Conditional Random Field (CRF). We present two approximate inference algorithms based on belief propagation, as well as a graph-cut-based approach not yet applied in this context. To demonstrate the performance of our approach, we present the classification results derived for a standard benchmark dataset. These results clearly indicate that the graph-cut-based method is able to retrieve a labeling of higher likelihood in only a fraction of the time needed for the other approaches. The higher likelihood, in turn, translates into a significant gain in the accuracy of the obtained classification.

Mots clés: Point cloud; classification; spatial regularization; belief propagation; graph-cut

Domaines: Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]

Loic Landrieu, Clément Mallet, Martin Weinmann. Comparison of belief propagation and graph-cut approaches for contextual classification of 3D lidar point cloud data. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2017, Fort Worth, Texas, United States. ⟨hal-01500777⟩