Calibration of depth cameras using denoised depth images
This addresses calibration challenges for depth cameras like Kinect and PMD, enabling more accurate applications in scientific and commercial research, though it is incremental.
The paper tackles the problem of calibrating depth cameras with low-resolution images by proposing a scheme that denoises depth measurements and uses them for calibration, achieving significantly better results than traditional methods.
Depth sensing devices have created various new applications in scientific and commercial research with the advent of Microsoft Kinect and PMD (Photon Mixing Device) cameras. Most of these applications require the depth cameras to be pre-calibrated. However, traditional calibration methods using a checkerboard do not work very well for depth cameras due to the low image resolution. In this paper, we propose a depth calibration scheme which excels in estimating camera calibration parameters when only a handful of corners and calibration images are available. We exploit the noise properties of PMD devices to denoise depth measurements and perform camera calibration using the denoised depth as an additional set of measurements. Our synthetic and real experiments show that our depth denoising and depth based calibration scheme provides significantly better results than traditional calibration methods.