Abstract: A method for AI-driven augmented reality mentoring includes determining semantic features of objects in at least one captured scene, determining 3D positional information of the objects, combining information regarding the identified objects with respective 3D positional information to determine at least one intermediate representation, completing the determined intermediate representation using machine learning to include additional objects or positional information of the objects not identifiable from the at least one captured scene, determining at least one task to be performed and determining steps to be performed using a knowledge database, generating at least one visual representation relating to the determined steps for performing the at least one task, determining a correct position for displaying the at least one visual representation, and displaying the at least one visual representation on the see-through display in the determined correct position as an augmented overlay to the view of the at least

Abstract: A method, apparatus and system for determining change in pose of a mobile device include determining from first ranging information received at a first and a second receiver on the mobile device from a stationary node during a first time instance, a distance from the stationary node to the first receiver and the second receiver, determining from second ranging information received at the first receiver and the second receiver from the stationary node during a second time instance, a distance from the stationary node to the first receiver and second receiver, and determining from the determined distances during the first time instance and the second time instance, how far and in which direction the first receiver and the second receiver moved between the first time instance and the second time instance to determine a change in pose of the mobile device, where a position of the stationary node is unknown.

Abstract: A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.

Abstract: Embodiments of the present invention generally relate to computer aided rebar measurement and inspection systems. In some embodiments, the system may include a data acquisition system configured to obtain fine-level rebar measurements, images or videos of rebar structures, a 3D point cloud model generation system configured to generate a 3D point cloud model representation of the rebar structure from information acquired by the data acquisition system, a rebar detection system configured to detect rebar within the 3D point cloud model generated or the rebar images or videos of the rebar structures, a rebar measurement system to measure features of the rebar and rebar structures detected by the rebar detection system, and a discrepancy detection system configured to compare the measured features of the rebar structures detected by the rebar detection system with a 3D Building Information Model (BIM) of the rebar structures, and determine any discrepancies between them.

Abstract: Embodiments of the present invention generally relate to computer aided rebar measurement and inspection systems. In some embodiments, the system may include a data acquisition system configured to obtain fine-level rebar measurements, images or videos of rebar structures, a 3D point cloud model generation system configured to generate a 3D point cloud model representation of the rebar structure from information acquired by the data acquisition system, a rebar detection system configured to detect rebar within the 3D point cloud model generated or the rebar images or videos of the rebar structures, a rebar measurement system to measure features of the rebar and rebar structures detected by the rebar detection system, and a discrepancy detection system configured to compare the measured features of the rebar structures detected by the rebar detection system with a 3D Building Information Model (BIM) of the rebar structures, and determine any discrepancies between them.