David Ferstl. Learning Depth Calibration of Time-of-Flight Camera

David Ferstl. 2015. 12 p.We present a novel method for automatically calibrating modern consumer Timeof-Flight (ToF) cameras. Usually, these sensors come equipped with an integrated color camera. Albeit they deliver acquisitions at high frame rates they typically suffer from incorrect calibration and low accuracy due to multiple error sources. Using information from both cameras and a simple planar target, we will show how to accurately calibrate both color and depth cameras, and tackle most error sources inherent to ToF technology in a unified calibration framework. Automatic feature detection minimizes user interaction during calibration. We utilize a Random Regression Forest to optimize the manufacturer-supplied depth measurements. We show the improvements to commonly used depth calibration methods in a qualitative and quantitative evaluation of multiple scenes acquired by an accurate reference system for the application of dense 3D reconstruction.