Clustering Quality Measures for Point Cloud Segmentation Tasks

This paper presents improved weighted measures for a point cloud segmentation quality evaluation. They provide more reliable and intuitive appraisal as well as more representative classification characteristics. The new measures are compared with the existing ones: based on classification, and based on information theory. The experiments and measures evaluation were performed for the recently outstanding fresh planes segmentation method. Experiments results showed that newly elaborated measures provide a researcher with distinguished information about segmentation output. This paper introduces recommendations for quality measures adjustment to a particular planar fragments detection problem, what implies contributions for effective development of such methods.

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