Key Points Positioning: A Two-Stage Algorithm For Single-view Point Cloud of Human Back Based on Point-wise Network

Abstract

Point cloud data is a collection of massive points containing the spatial position of each point on the target surface, which contains abundant spatial information. At present, it is also applied to the digital modeling of human surface in medical imaging, as the data basis for subsequent human body measurement, morphology estimation and data analysis. Among them, the key points is defined as the landmark position of the surface morphology analysis, those key points provides a reference position for the analysis work, and also reflects the symmetry of the body to a certain extent and morphology information. Aiming at the back shape analysis in clinical diagnosis, this paper proposes a two-stage key points positioning scheme of coarse segmentation and fine positioning. We design and build an pointwise artificial neural network to roughly locate the body part, in this part, we propose a maximum pooling module based on spatial location coding to express local features more strongly. Farther, we propose a gray distance and curvature based operator to match the position of key points. The experiment, shows that our method can effectively enhance the distinctiveness of features and meanwhile, reduce the influence from background.