Abstract
This paper proposes a depth measurement error model for consumer depth cameras such as the Microsoft Kinect, and a corresponding calibration method. These devices were originally designed as video game interfaces, and their output depth maps usually lack sufficient accuracy for 3D measurement. Models have been proposed to reduce these depth errors, but they only consider camera-related causes. Since the depth sensors are based on projector-camera systems, we should also consider projector-related causes. Also, previous models require disparity observations, which are usually not output by such sensors, so cannot be employed in practice. We give an alternative error model for projector-camera based consumer depth cameras, based on their depth measurement algorithm, and intrinsic parameters of the camera and the projector; it does not need disparity values. We also give a corresponding new parameter estimation method which simply needs observation of a planar board. Our calibrated error model allows use of a consumer depth sensor as a 3D measuring device. Experimental results show the validity and effectiveness of the error model and calibration procedure.
Highlights
Various consumer depth cameras such as the Microsoft Kinect V1/V2, Asus Xtion, etc. have been released
In this paper, we focus on projector-camera based consumer depth cameras and propose a depth error correction method based on their depth measurement algorithm
We have proposed and evaluated a depth error model for projector-camera based consumer depth cameras such as the Kinect, and
Summary
Various consumer depth cameras such as the Microsoft Kinect V1/V2, Asus Xtion, etc. have been released. Darwish et al [8] proposed a calibration algorithm that considers both camera and projectorrelated parameters for Kinect These methods as well as other previous methods require disparity observations, and these are not generally provided by such sensors. To provide straightforward procedures for calibration and error compensation for depth data, including previously captured data, our method introduces a parametric error model that considers (i) both camera and projector distortion, and (ii) errors in the parameters used to convert disparity observations to actual disparity. We note that the calibration method introduced in this paper is designed for Kinect because it is the most common projector-camera based depth sensor.
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