Fast Segmentation of Range Images into Planar Regions by Scan Line Grouping

In this paper we present a novel technique for rapidly partitioning surfaces in range images into planar patches. Essential for our segmentation method is the observation that, in a scan line, the points belonging to a planar surface form a straight line segment. On the other hand, all points on a straight line segment surely belong to the same planar surface. Based on this observation, we rst divide each scan line into straight line segments and subsequently consider only the set of line segments of all scan lines as segmentation prim-itives. We have developed a simple link-based data structure to eeciently represent line segments and their neighborhood relationship. The principle of our segmentation method is region growing. Three neighboring line segments satisfying an optimality criterion are selected as a seed region, and then a growing is carried out around the seed region. We use a noise variance estimation to automatically set some thresholds so that the algorithm can adapt to the noise conditions of diierent range images. The proposed algorithm has been tested on real range images acquired by two diierent range sensors. Experimental results show that the proposed algorithm is fast and robust.