Fast geometric point labeling using conditional random fields
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Validating vision and robotic algorithms for dynamic real world environments
SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
Conditional random fields for urban scene classification with full waveform LiDAR data
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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We proposed using Conditional Random Fields with adaptive data reduction for the classification of 3D point clouds acquired from a Riegl Terrestrial laser scanner. The training and inference of the acquired large outdoor urban data can be time consuming. We approach the problem by computing an adaptive support region for each data point using 3D scale theory. For training and inference of the discriminative Conditional Random Fields, smaller set of data samples that contains relevant information within the support region is selected instead of using all point cloud data. We tested the algorithm on synthetically generated data and urban point clouds data acquired from the laser scanner. The computed support region is also used in feature extraction for urban point clouds data. The results showed improvement in the training and inference rate while maintaining comparable classification accuracy.