Body measurement based on a multiview structured light 3D imaging system

In this paper, a multi-view structured light three dimensional (3D) imaging system is descried, which can perform rapid 3D reconstruction of the human body and automatically extract anthropometric data from it. This system contains 12 sets of 3D imaging sensors distributed on four pillars. Each 3D imaging sensor consists of a binocular stereo system, an Infrared laser projector and a synchronous control system based on the Field Programmable Gate Array (FPGA). The projector provides phase-shifting fringe patterns and gray code for the binocular stereo system to make 3D reconstruction. The FPGA control system enables the sensor to achieve high speed scanning. A two-step calibration method is used to calibrate the internal and external parameters of each 3D imaging sensor and external parameters between these sensors. After the 3D human body data acquisition, major body joints will be extracted as key-points. In this process, the initial location of these key-points are extracted based on a deep learning method, and then they are further corrected with local point cloud analysis. With the assistance of these key-points, the anthropometric data, such as distances (lengths, breadths, heights) and circumferences of the human body, can be calculated from its 3D data. Based on the techniques described above, the multi-view 3D imaging system can complete the whole body scanning in 2 seconds and automatically measure more than sixty dimensional data after analyzing the reconstructed 3D human data.