Exemplar-Based 3D Shape Segmentation in Point Clouds

This paper addresses the problem of automatic 3D shape segmentation in point cloud representation. Of particular interest are segmentations of noisy real scans, which is a difficult problem in previous works. To guide segmentation of target shape, a small set of pre-segmented exemplar shapes in the same category is adopted. The main idea is to register the target shape with exemplar shapes in a piece-wise rigid manner, so that pieces under the same rigid transformation are more likely to be in the same segment. To achieve this goal, an over-complete set of candidate transformations is generated in the first stage. Then, each transformation is treated as a label and an assignment is optimized over all points. The transformation labels, together with nearest-neighbor transferred segment labels, constitute final labels of target shapes. The method is not dependent on high-order features, and thus robust to noise as can be shown in the experiments on challenging datasets.