Comparison and classification of 3D objects surface point clouds on the example of feet
One of the main tasks of shoe manufacturing is the production of well fitting shoes for different specialized markets. The key to conduct this properly is the analysis of the factors that influence the variations of the foot shape. In this paper methods and results of clustering and analysis of 3D foot surfaces are presented. The data were collected from a study with more than 12,000 feet that have been laser-scanned. The database contains point clouds acquired from persons coming from different regions of the world. Furthermore, additional personal data were collected. Two different methods for quantifying the similarity of 3D surface point clouds are therefore developed. The first method generally works on nearly arbitrary 3D surface point clouds, while the second one is specialized on foot data sets. These similarity measures were used on the data sets of the foot-shape study, together with clustering and feature quality evaluation methods. The purpose was to obtain information about the impact of, and the relationship among, the different factors influencing the shape of a foot. Through the observations of the experiments presented here it was possible to build up a hierarchy of different levels of feature-groups determined by their impact on the foot shape. Furthermore, an investigation of the quality and amount of impact of the features, according to their ability to separate specific subgroups of persons, is shown. Based on these results it was possible to select those features, which result in the largest effect when designing shoes for e.g. the Asian versus European markets.
classification 3d foot measurement laser scanner 3d point cloud unsupervised learning