Recalibration of Structured-Light RGB-D Cameras with Parametric Depth Error Correction

Structured-light RGB-D cameras have been widely used in various applications. However, due to the deformation of internal camera parts, their depth estimation accuracy degrades with time. While it is easy to calibrate the camera parameters, updating the calibrated parameters to the camera firmware is difficult. Therefore, existing methods compensate for the depth measurements with different error correction functions. At present, as there are no simple and accurate parametric error correction methods, non-parametric calibration methods must be used when accurate depth measurements are required. The main drawback of such nonparametric approaches is that they require a large number of calibration images to calibrate a large error correction lookup tables. In this paper, we propose a simple parametric depth error correction model based on Taylor-series approximation of depth measurement equations. Experimental results show that the proposed method outperforms other parametric approaches and achieves results comparable to the state-of-the-art nonparametric method although the proposed method uses only nine parameters.