Range Image Segmentation for Modeling and Object Detection in Urban Scenes

Abstract

We present fast and accurate segmentation algorithms of range images of urban scenes. The utilization of these algorithms is essential as a pre-processing step for a variety of tasks, that include 3D modeling, registration, or object recognition. The accuracy of the segmentation module is critical for the performance of these higher-level tasks. In this paper, we present a novel algorithm for extracting planar, smooth non-planar, and non-smooth connected segments. In addition to segmenting each individual range image, our methods also merge registered segmented images. That results in coherent segments that correspond to urban objects (such as facades, windows, ceilings, etc.) of a complete large scale urban scene. We present results from experiments of one exterior scene (Cooper Union building, NYC) and one interior scene (Grand Central Station, NYC).