Hybrid Kinect Depth Map Refinement for Transparent Objects
Görkem Saygılı, Laurens van der Maaten, Emile A. Hendriks · 2014
Depth sensors such as Kinect fail to find the depth of transparent objects which makes 3D reconstruction of such objects a challenge. The refinement algorithms for Kinect depth maps either do not address transparency or they only provide sparse depth on such objects which is inadequate for dense 3D reconstruction. In order to solve this problem, we propose a fully-connected CRF based hybrid refinement algorithm. We incorporate stereo cues from cross-modal stereo between IR and RGB cameras of the Kinect and Kinect's depth map. Our algorithm does not require any additional cameras and still provides dense depth estimations of transparent objects and specular surfaces with high accuracy.