Cutting-Plane Training of Non-associative Markov Network for 3D Point Cloud Segmentation

We address the problem of object class segmentation of 3D point clouds. Each point of a cloud should be assigned a class label determined by the category of the object it belongs to. Non-associative Markov networks have been applied to this task recently. Indeed, they impose more flexible constraints on segmentation results in contrast to the associative ones. We show how to train non-associative Markov networks in a principled manner using the structured Support Vector Machine (SVM) formalism. In contrast to prior work we use the kernel trick which makes our method one of the first non-linear methods for max-margin Markov Random Field training applied to 3D point cloud segmentation. We evaluate our method on airborne and terrestrial laser scans. In comparison to the other non-linear training techniques our method shows higher accuracy.