We present a min-cut based method of segmenting objects in point clouds. Given an object location, our method builds a k-nearest neighbors graph, assumes a background prior, adds hard foreground (and optionally background) constraints, and finds the min-cut to compute a foreground-background segmentation. Our method can be run fully automatically, or interactively with a user interface. We test our system on an outdoor urban scan, quantitatively evaluate our algorithm on a test set of about 1000 objects, and compare to several alternative approaches.

@inproceedings{Golovinskiy:2009:MBS,
   author = "Aleksey Golovinskiy and Thomas Funkhouser",
   title = "Min-Cut Based Segmentation of Point Clouds",
   booktitle = "IEEE Workshop on Search in 3D and Video (S3DV) at ICCV",
   year = "2009",
   month = sep
}