Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction

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.