A novel approach based on cluster-group for classification of 3D residential scene

To understand scenes and help autonomous robots and cars, researchers´ attention is directed through the problem of classifying 3D point cloud. In this paper, we present a novel approach to semantically segment 3D point cloud of residential scenes captured by a lidar sensor. Our approach is based on a dual-scale analysis: a small-scale clustering and a large-scale grouping. Features used to train our AdaBoost classifier are then extracted from clusters and groups. We evaluate our method with a challenging lidar data set. The result shows our approach can classify scene objects accurately.