Shape-based Recognition of 3D Point Clouds in Urban Environments

This paper investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The system is decomposed into four steps: locating, segmenting, characterizing, and classifying clusters of 3D points. Specifically, we first cluster nearby points to form a set of potential object locations (with hierarchical clustering). Then, we segment points near those locations into foreground and background sets (with a graph-cut algorithm). Next, we build a feature vector for each point cluster (based on both its shape and its context). Finally, we label the feature vectors using a classifier trained on a set of manually labeled objects. The paper presents several alternative methods for each step. We quantitatively evaluate the system and tradeoffs of different alternatives in a truthed part of a scan of Ottawa that contains approximately 100 million points and 1000 objects of interest. Then, we use this truth data as a training set to recognize obje...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Aleksey Golovinskiy, Vladimir G. Kim, Thomas Funkhouser
• Keywords: 3D Point Clouds, Computer Vision, ICCV 2009, Rban Environments, Shape Recognition