Research On Several Problems In Point Cloud Data Processing Of Human Body

3D scanning technology has found it application in inverse engineering, simulation, medical training, surgery, anthropometrics and garment engineering.3D scanned data have gain concentrate research with the development of3D scanning technology.Point cloud in contrasts to mesh based model have the advantage of no redundancy data gains the concentrate research. The point based3d human model is more feasible to obtain any level of anthropometrics information than the meshed one.There relatively fewer researches direct on point based3d scanned human body for anthropometrics which mainly relay on the general point cloud methods or turn the point cloud model to mesh for further analysis.Although the research on point based model had improved with the point representation geometry, there relatively few technology on point cloud of human body (HB) perfectly fulfills the needs of anthropometrics and garment industry. The research on specialized point cloud human body is lag behind. For example the customization of garment and the analysis (pressure etc.) of functional clothing on human body. virtual try on true3D human model and decimally accurate show of in3d garment designed pattern in sense of true accurate in stead of visual fidelity. The current research on point cloud is on general point cloud of garment engineer and the anthropometrics. How to adopts and improves the level-of-art methods on mesh based model or general point cloud model to scanned HB in order to meet the needs of anthropometrics or garment engineering.This paper is concentrated on the researches of point cloud of human body which aim to form effective specialized methods for anthropometrics or garment engineering.The main contributions of this work are on the followings:1. Presents a global smooth a filling holes strategy for HBS data. And the filled data are more faith to the real human body and could meet the need for anthropometrics.2. Proposes a simple and effective feature preserved simplification strategy for points cloud. It identifies feature points and then simplifies by self-adopted weight. During any level of simplification the important feature point of anthropometrics could preserved. With constraint to HBS simplification, the algorithm could preserve the anthropometrics key points effectively and efficiently even in sharp simplification rate. The simplification process is controllable and more feasible and used the feature set could extraction the longitudinal curves (in point set) of human for garment design at any level of detail with convenient, which could offer reliable data further shape analysis. The algorithms avoided the thin part attenuation which caused topological missing even in sharp simplification.3. Presents a skeleton curve extracted algorithm directly on scanned Human body point cloud with large boundary noises and gaps. The algorithm used the sliced embedding strategy to decrease computational costs, and it prevents the skeleton contraction off the central of model and the algorithm is insensitive to boundary noises. It is very important for analysis and classifies human body type.Also describes a low costs method to for generating dynamical human bodypoint cloud with depth serials. And finally presents human head tracking methodswith the dynamical point cloud as Priori knowledge guided. It has the ability oftracking error self-detected and recovery. It is insensitive to the texture of face eithercaused by illumination and different people.The achievements have benefits to3D computer aided garment design, anthropometrics for analysis of human body, which are very important for analysis Chinese body shape and form standard digital mannequin for Chinese body shape and also has benefit to dynamically show on accurate3D human body.