  We present an efficient and scalable algorithm for segmenting 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
using 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 combination of depth and
color. Due to its incremental processing, 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 numerous experimental
results on challenging sequences from our own as well as public RGBD data sets.
