Adaptivity of conditional random field based outdoor point cloud classification

Abstract

In this paper we present how adaptable learned models of graphical models are and how they can be used for classification tasks of 3D laser point clouds with different distributions and density. In order to model the contextual information we use a pair-wise conditional random field and an adaptive graph down-sampling method based on voxel grids. As feature we apply the rotation invariant histogram-of-oriented-residuals operator to describe the local point cloud distribution. We validate the approach with data collected from different laser range finders with varying point cloud distribution and density. Our experiments imply, that conditional random field models learned from one dataset can be applied to another dataset without a significant loss of precision.