Non-Associative Markov Networks for 3D Point Cloud Classification

ЗаголовокNon-Associative Markov Networks for 3D Point Cloud Classification
Тип публикацииConference Paper
Год публикации2010
АвторыShapovalov R, Velizhev A, Barinova O
Refereed DesignationRefereed
КонференцияPhotogrammetric Computer Vision and Image Analysis
Язык публикацииEnglish
Ключевые словаClassification, LIDAR, Markov Random Fields, Point Cloud, Random Forest

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.

Ключ цитированияShapovalov2010