NON-ASSOCIATIVE MARKOV NETWORKS FOR 3D POINT CLOUD CLASSIFICATION

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.

[1]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[2]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  W. Freeman,et al.  Generalized Belief Propagation , 2000, NIPS.

[5]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Ben Taskar,et al.  Learning associative Markov networks , 2004, ICML.

[9]  George Vosselman,et al.  Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds , 2004 .

[10]  Ben Taskar,et al.  Discriminative learning of Markov random fields for segmentation of 3D scan data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Dániel Tóvári.  Segmentation Based Classification of Airborne Laser Scanner Data , 2006 .

[12]  David P. Helmbold,et al.  Aerial LiDAR Data Classification Using Support Vector Machines (SVM) , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[13]  Wolfram Burgard,et al.  Robust 3D scan point classification using associative Markov networks , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[14]  Vladimir Kolmogorov,et al.  Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Pushmeet Kohli,et al.  P3 & Beyond: Solving Energies with Higher Order Cliques , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  David P. Helmbold,et al.  Aerial Lidar Data Classification using AdaBoost , 2007, Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007).

[17]  Wolfram Burgard,et al.  Instace-Based AMN Classification for Improved Object Recognition in 2D and 3D Laser Range Data , 2007, IJCAI.

[18]  Martial Hebert,et al.  Directional Associative Markov Network for 3-D Point Cloud Classification , 2008 .

[19]  Nicolas David,et al.  LIDAR Data Classification using Hierarchical K-means clustering , 2008 .

[20]  Wolfram Burgard,et al.  Unsupervised discovery of object classes from range data using latent Dirichlet allocation , 2009, Robotics: Science and Systems.

[21]  C. Mallet,et al.  AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS , 2009 .

[22]  Martial Hebert,et al.  Contextual classification with functional Max-Margin Markov Networks , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Martial Hebert,et al.  Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields , 2009, 2009 IEEE International Conference on Robotics and Automation.

[24]  James J. Little,et al.  A Hybrid Conditional Random Field for Estimating the Underlying Ground Surface From Airborne LiDAR Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Avideh Zakhor,et al.  Classifying urban landscape in aerial LiDAR using 3D shape analysis , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[26]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.