Steven Hickson

College of Computing, Georgia Tech

shickson [at] gatech.edu

Stan Birchfield

Microsoft

stanleyb [at] microsoft.com

Irfan Essa

College of Computing, Georgia Tech

irfan [at] cc.gatech.edu

Henrik Christensen

College of Computing, Georgia Tech

hic [at] cc.gatech.edu

We present an efficient and scalable algorithm for seg- menting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach. Our algorithm processes a moving window over several point clouds to group similar regions over a graph, resulting in an initial over-segmentation. These regions are then merged to yield a dendrogram us- ing agglomerative clustering via a minimum spanning tree algorithm. Bipartite graph matching at a given level of the hierarchical tree yields the final segmentation of the point clouds by maintaining region identities over arbitrarily long periods of time. We show that a multistage segmentation with depth then color yields better results than a linear com- bination of depth and color. Due to its incremental process- ing, our algorithm can process videos of any length and in a streaming pipeline. The algorithm’s ability to produce robust, efficient segmentation is demonstrated with numer- ous experimental results on challenging sequences from our own as well as public RGBD data sets.

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The code requires OpenCV and PCL and should be compatible in Windows and Linux with the most time-of-flight and projected infrared devices. It can be downloaded at https://github.com/StevenHickson/4D_Segmentation

@inproceedings{2014-Hickson-EHGSRV,
       Author = {Steven Hickson and Stan Birchfield and Irfan Essa and Henrik Christensen},
       Booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
       Month = {June},
       Organization = {IEEE Computer Society},
       Title = {Efficient Hierarchical Graph-Based Segmentation of RGBD Videos},
       Year = {2014}
}

The authors would like to thank Prof. Mubarak Shah and his PhD Student Gonzalo Vaca-Castano for their mentorship and guidance to the primary author of this paper, when he participated in the National Science Foundation funded " REU Site: Research Experience for Undergraduates in Computer Vision" (#1156990) in 2012 at University of Central Florida's Center for Research in Computer Vision .

In addition, we would also like to thank TUM and NYU for providing datasets. Details and links forthcoming.

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