A flexible calibration method of multiple Kinects for 3D human reconstruction

In this paper, we present a simple yet effective calibration method for multiple Kinects, i.e. a method that finds the relative position of RGB-depth cameras, as opposed to conventional methods that find the relative position of RGB cameras. We first find the mapping function between the RGB camera and the depth camera mounted on one Kinect. With such a mapping function, we propose a scheme that is able to estimate the 3D coordinates of the extracted corners from a standard calibration chessboard. To this end, we are able to build the 3D correspondences between two Kinects directly. This simplifies the calibration to a simple Least-Square Minimization problem with very stable solution. Furthermore, by using two mirrored chessboard images on a thin board, we are able to calibrate two Kinects facing each other, something that is intractable using traditional calibration methods. We demonstrate our proposed method with real data and show very accurate calibration results, namely less than 7mm reconstruction error for objects at a distance of 1.5m, using around 7 frames for calibration.