A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (a) a common set of 40 laser range nder images and 40 structured light scanner images that have manually speci ed ground truth and (b) a set of de ned performance metrics for instances of correctly segmented, missed and noise regions, over-and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the speci ed ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches.
In REF , a machine segmentation (MS) of an image is compared to the ground-truth specification to count instances of correct segmentation, under-segmentation, over-segmentation, missed regions, and noise regions.
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Trans. Pattern Anal. Mach. Intell.
IEEE Trans. Pattern Anal. Mach. Intell.
Computer Science