Automated Performance Evaluation of Range Image Segmentation Algorithms

We describe a framework for evaluating the performance of range image segmentation algorithms. The framework is intended to be fully automated and to allow objective and relevant comparison of performance. In principle, it could be used to evaluate general image segmentation algorithms, but the framework is demonstrated here using range images. The framework implemented is in a publicly available tar file that includes images, code, and shell scripts. The primary performance metric is the number of regions correctly segmented. The definition of “correctly segmented” is parameterized on the percent of mutual overlap between a segmented region in an image and its corresponding region in a ground truth specification of the image. This work should make it possible to directly compare the performance of range image segmentation algorithms intended either for planar-surface scenes or for curved-surface scenes.