Reduced Calibration Strategy Using a Basketball for RGB-D Cameras

RGB-D cameras produce depth and color information commonly used in the 3D reconstruction and vision computer areas. Different cameras with the same model usually produce images with different calibration errors. The color and depth layer usually requires calibration to minimize alignment errors, adjust precision, and improve data quality in general. Standard calibration protocols for RGB-D cameras require a controlled environment to allow operators to take many RGB and depth pair images as an input for calibration frameworks making the calibration protocol challenging to implement without ideal conditions and the operator experience. In this work, we proposed a novel strategy that simplifies the calibration protocol by requiring fewer images than other methods. Our strategy uses an ordinary object, a know-size basketball, as a ground truth sphere geometry during the calibration. Our experiments show comparable results requiring fewer images and non-ideal scene conditions than a reference method to align color and depth image layers.

Automated summary

RGB-D cameras are commonly used in 3D reconstruction and computer vision, but calibration errors can affect alignment. In this work, we propose a novel strategy that simplifies calibration by using a known-sized ordinary object as a calibration reference, requiring fewer images and achieving comparable results even in non-ideal conditions.