University of Houston

The Microsoft Kinect is a novel sensor that besides color images it also returns the actual distance of a captured scene from the camera. Its depth sensing capabilities, along with its affordable, commercial-type availability led to its quick adaptation for research and applications in Computer Vision and Graphics. Recently, multi-Kinect systems are being introduced in order to tackle problems like body scanning, scene reconstruction, and object detection. Multiple-cameras configurations however, must first be calibrated on a common coordinate system, i.e. the relative position of each camera, needs to be estimated with respect to a global origin. Up to now, this has been addressed by applying well-established calibration methods, developed for conventional cameras. Such approaches do not take advantage of the additional depth information, and disregard the quantization error model introduced by the depth resolution specifications of the sensor. We propose a novel algorithm for calibrating a pair of depth sensors, based on a recovered affine transformation from very few 3D point correspondences, refined under a non-rigid registration, that accounts for the non-linear sensor acquisition error. This formulation is further complemented by proposing two different ways of efficiently capturing candidate calibration points. Qualitative 3D registration results show significant improvement over the conventional rigid calibration method, and highlight the potential for advanced and more accurate multi-sensor configurations.