Adaptive 3D sensing system based on variable magnification using stereo vision and structured light

In this paper, the design and implementation of a 3D sensing system with adjustable zoom based on stereo vision and structured light is discussed. The proposed system consists of a stereo camera with an illumination projector and zoom lenses to collect high-resolution data from a distant object. A calibration target suitable for zoom lens calibration is designed along with the development of a zoom lens calibration method for a stereo camera based on linear and non-linear models. The robust zoom lens control system, based on a precise automation hardware and image processing technique, is developed to produce high-quality images. Furthermore, normalized cross correlation (NCC), being robust with intensity offsets and contrast changes, is used for stereo matching of the coded images to generate the reconstruction results using a linear equation method. The integration of NCC with structured light and epipolar geometry for the proposed 3D sensing system further yields accurate 3D reconstruction results as demonstrated in the experimental results of this research. The complex relationship of the parameters of each camera on variable zoom is determined using optimal fitting. Linear interpolation is used to estimate calibration data at an arbitrary zoom setting to validate zoom lens calibration. The 3D reconstruction on variable zoom shows that higher lens magnification results in a more accurate 3D sensing system.