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Shape-based Recognition of 3D Point Clouds in Urban Environments
International Conference on Computer Vision (ICCV), September 2009

Aleksey Golovinskiy, Vladimir G. Kim, Thomas Funkhouser


Abstract

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 objects amidst approximately 1 billion points of the remainder of the Ottawa scan.

Citation (BibTeX)

Aleksey Golovinskiy, Vladimir G. Kim, and Thomas Funkhouser. Shape-based Recognition of 3D Point Clouds in Urban Environments. International Conference on Computer Vision (ICCV), September 2009.

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