Non-Associative Markov Networks for 3D Point Cloud Classification

TitleNon-Associative Markov Networks for 3D Point Cloud Classification
Publication TypeConference Paper
Year of Publication2010
AuthorsShapovalov R, Velizhev A, Barinova O
Refereed DesignationRefereed
Conference NamePhotogrammetric Computer Vision and Image Analysis
Publication LanguageEnglish
KeywordsClassification, LIDAR, Markov Random Fields, Point Cloud, Random Forest
Abstract

The problem of laser scan analysis gained significant attention within the last decade. The standard approach to point cloud classification utilizes Markov Random Fields (MRF). Usually, a subclass of MRFs, Associative Markov Networks (AMNs), are used. In AMN the pairwise potential function is constant for a pair of different class labels. In some cases this constraint is too rigorous since it does not allow expressing some natural interactions between objects, such as “roof is likely to be above the ground”. In this work we use the general form of pairwise potentials instead. We show how to perform a tractable inference in such network using a message-passing based algorithm. Oversegmentation technique is used to subsample a scan; this helps to eliminate noise, improve efficiency and makes possible to use natural edge features. Experiments on two datasets obtained by an airborne laser scanner show that non-associative Markov networks usage leads to improvement in classification accuracy.

URLhttp://graphics.cs.msu.ru/sites/default/files/download/Shapovalov-et-al-PCV2010.pdf
Citation KeyShapovalov2010