Multiple consumer-grade depth camera registration using everyday objects

Abstract The registration of multiple consumer-grade depth sensors is a challenging task due to noisy and systematic distortions in depth measurements. Most of the existing works heavily rely on large number of checkerboard observations for calibration and registration of multiple depth cameras, which is tedious and not flexible. In this paper, we propose a more practical method for conducting and maintaining registration of multi-depth sensors, via replacing checkerboards with everyday objects found in the scene, such as regular furniture. Particularly, high quality pre-scanned 3D shapes of standard furniture are used as calibration targets. We propose a unified framework that jointly computes the optimal extrinsic calibration and depth correction parameters. Experimental results show that our proposed method significantly outperforms state-of-the-art depth camera registration methods.