Dissimilarity to class medoids as features for 3D point cloud classification

Sylvain Chabanet (1) , Valentin Chazelle (1) , Philippe Thomas (1) , Hind Bril El-Haouzi (1)

1 CRAN - Centre de Recherche en Automatique de Nancy

Several sawmill simulators exist in the forest-product industry. They are able to simulate the sawing of a log to generate the set of lumbers that would be obtained by transforming a log at a sawmill. In particular, such simulators are able to use a 3D scan of the exterior shape of the logs as input for the simulation. However, it was observed that they can be computationally intensive. Therefore, several authors have proposed to use Artificial Intelligence metamodel, which, in general, can make predictions extremely fast once trained. Such models can approximate the results of a simulator using a vector of descriptive features representing a log, or, alternatively, the full 3D log scans. This paper proposes to use dissimilarity to representative log scans as features to train a Machine Learning classifier. The concept of class Medoids as representative elements of a class will be presented, and a Simlarity Discrimant Analysis was chosen as a good candidate ML classier. This classifier will be compared with two others models studied by the authors.

Mots clés: Similarity Discriminant Analysis; Sawmill simulation; Artificial Intelligence; Iterative Closest Point dissimilarity; Medoids

Domaines: Informatique [cs]; Sciences de l'ingénieur [physics]

Sylvain Chabanet, Valentin Chazelle, Philippe Thomas, Hind Bril El-Haouzi. Dissimilarity to class medoids as features for 3D point cloud classification. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.573-581, ⟨10.1007/978-3-030-85906-0_62⟩. ⟨hal-03334543⟩