Easy Extrinsic Calibration of VR System and Multicamera Based MarkerLess Motion Capture System This paper proposes a novel easy extrinsic calibration algorithm for an offtheshelf VR system and a multicamera based markerless motion capture system. To realize interactions between 3D user motions and virtual objects reconstructed from multiview videos in a common 3D space, the extrinsic calibration of the VR system and the multiple cameras must be conducted beforehand. This calibration, which involves estimating the pose and position of each coordinate system, is a key technology for handling 3D information in a system with various type of input sources. In general, extrinsic calibration is carried out by identifying and utilizing some common 3D points. However, since most of offtheshelf VR systems do not include any imaging device, it is difficult to apply conventional automatic calibration approaches as there are no common points shared with the cameras. Against this problem, this paper introduces an easy calibration algorithm by generating corresponding points from the trajectories of the useru0027s motion with VR devices and 3D human pose reconstructed from multiview videos. Our study provides the following two contributions; (1) our method does not need to introduce additional devices, such as a chessboard and (2) our method does not need manual processes as the extrinsic parameters are automatically estimated. We demonstrate the performance of the proposed method in a practical scenario.

A Novel Robust Approach for CorrespondenceFree Extrinsic Calibration Extrinsic calibration is a necessary step when using heterogeneous sensors for robotics applications. Most existing methods work under the assumption that the prior data correspondence is known. Considering data loss and false measurements, the correspondence may not be accessible in practice. To solve this problem without knowing the correspondence, several probabilistic methods have been proposed. However, an implicit restriction on input data limits their application. Therefore, in this paper, we propose a more stable correspondencefree method with two improvements that can relax the restrictions on inputs and improve the calibration accuracy. The first improvement finds consistent sets from raw inputs using screw invariants, which significantly improve the robustness in case of outliers and data loss. A new optimization method on matrix Lie group is proposed as the second improvement, which demonstrates better accuracy. The experimental results on both numerical and real data show the superiority and robustness of the proposed method.