Infrared structured light sensors are widely employed for control applications, gaming, acquisition of dynamic and static 3D scenes. Recent developments have lead to the availability on the market of low-cost sensors, like Kinect devices, which prove to be extremely sensitive to noise, light conditions, and the geometry of the scene.The paper presents a quality enhancement strategy for Kinect-acquired depth maps that corrects depth values via a set of local differential equations and interpolates the missing depth samples. The approach improves both density and accuracy of the generated 3D model under different light conditions and in the presence of cross-talk noise derived from other devices. Moreover, the paper introduces a new experimental reference dataset for Kinect denoising algorithms consisting in multiple acquisition under different noise conditions associated to a laser scan model of the scene (ground truth). HighlightsThe paper presents a denoising strategy for infrared structured light depth sensors.Errors are evaluated by measuring the edge mismatch between color and depth images.Evaluation datasets are proposed with ground-truth point clouds from a laser scanner.Multiple acquisitions were included for different noise and illumination conditions.Experimental results prove that the solution performs well and in real time.
['Simone Milani', 'Giancarlo Calvagno']
Correction and interpolation of depth maps from structured light infrared sensors