Calibration of depth cameras using denoised depth images

TL;DR

This paper introduces a novel depth calibration method for depth cameras that leverages denoised depth images to improve calibration accuracy, especially with limited calibration data.

Contribution

It proposes a depth calibration scheme that uses denoised depth images from PMD devices, outperforming traditional methods with fewer calibration images.

Findings

Denoising improves calibration accuracy.

Method outperforms traditional calibration techniques.

Effective with limited calibration data.

Abstract

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.