Hybridization of structured light and TimeofFlight sensing using maximum a posteriori Markov Random Fields The optimization of the 3D laser scanning system is a rapidly expanding field with applications ranging from autonomous driving to facial recognition. The two competing 3D sensing technologies of structured light (SL) and TimeofFlight (ToF) have inherent weaknesses with regards to their sensing capabilities: SL cameras are prone to errors at long ranges and have low depth resolution, and ToF cameras have scene dependent errors and are perpixel noisy. We propose a solution to overcome the contrasting weaknesses caused by multipath errors in ToF, and occlusion errors in SL. By combining both sets of data and applying belief propagation, we are able to decrease the effects of errors intrinsic to offtheshelf 3D laser scanners (using the Kinect V1 for SL and Kinect V2 for ToF).

A DualSLS for Efficient 3D Scanning of Dynamic Objects Structured lightbased 3D sensing techniques has been an important means for 3D digitization of real objects. However, to obtain a complete 3D model of target, multiple scans are demanded and that makes the operation timeconsuming. In this paper, an efficient 3D scanning system is introduced, which is composed of two structured light sensors. The two sensors are placed in opposite directions, and the target is placed between them. With a single scan operation, two point clouds can be obtained and aligned by the proposed system calibration parameters. For some objects with relative simple geometry like human hands, a complete 3D model can be obtained via single scan. But regarding to complex targets, by changing the target orientation several times and using the proposed point cloud registration method, complete 3D model can be obtained more efficiently. Experiments with different objects are implemented to verify feasibility and accuracy of the proposed method.