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


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.


Author = {Aleksey Golovinskiy and Vladimir G. Kim and Thomas Funkhouser }, 
Journal = {ICCV}, 
Title = {{Shape-based Recognition of 3D Point Clouds in Urban Environments}}, 
Year = {2009}}