Abstract: Systems for completing missing points in a three-dimensional (3D) point cloud. The systems include a 3D sensor for measuring point data of objects in a scene, a processor in connection with the 3D sensor and a memory storing a program and the point data, and a display monitor connected to the processor and the 3D sensor for displaying a completed 3D point cloud, wherein the processor executes instruction steps of the program. The instruction steps include acquiring the point data of the objects to generate the 3D point cloud, wherein the point data include a set of points on the objects, extracting planar segments from the 3D point cloud, identifying connectivity relations and missing points among the planar segments, and filling the missing points in the 3D point cloud to generate the completed 3D point cloud using the planar segments and the connectivity relations.

Abstract: Systems for completing missing points in a three-dimensional (3D) point cloud. The systems include a 3D sensor for measuring point data of objects in a scene, a processor in connection with the 3D sensor and a memory storing a program and the point data, and a display monitor connected to the processor and the 3D sensor for displaying a completed 3D point cloud, wherein the processor executes instruction steps of the program. The instruction steps include acquiring the point data of the objects to generate the 3D point cloud, wherein the point data include a set of points on the objects, extracting planar segments from the 3D point cloud, identifying connectivity relations and missing points among the planar segments, and filling the missing points in the 3D point cloud to generate the completed 3D point cloud using the planar segments and the connectivity relations.

Abstract: A method determines dimensions in a scene by first acquiring a depth image of the scene acquired by a sensor, and extracting planes from the depth image. Topological relationships of the planes are determined. The dimensions are determined based on the planes and the topological relationships. A quality of the dimensions is evaluated using a scene type, and if the quality is sufficient outputting the dimensions, and otherwise outputting a guidance to reposition the sensor.

Abstract: A method of blending at least one virtual construction jobsite object and at least one real construction jobsite object in a dynamic augmented reality scene of a construction jobsite includes several steps. One step involves capturing a depth map image via a depth sensing device, and capturing a red, green, and blue (RGB) image via an RGB image-capturing device. Another step involves registering the depth map image and the RGB image to a common coordinate system. Yet another step involves projecting the at least one virtual construction jobsite object in a scene of the construction jobsite with the use of geographical information system (GIS) data, computer-aided design (CAD) data, or both types of data, to generate the augmented reality scene of the construction jobsite. And yet another step involves removing hidden surfaces of the at least one virtual construction jobsite object in the augmented reality scene.

Abstract: A method determines dimensions in a scene by first acquiring a depth image of the scene acquired by a sensor, and extracting planes from the depth image. Topological relationships of the planes are determined. The dimensions are determined based on the planes and the topological relationships. A quality of the dimensions is evaluated using a scene type, and if the quality is sufficient outputting the dimensions, and otherwise outputting a guidance to reposition the sensor.

Abstract: A method of blending at least one virtual construction jobsite object and at least one real construction jobsite object in a dynamic augmented reality scene of a construction jobsite includes several steps. One step involves capturing a depth map image via a depth sensing device, and capturing a red, green, and blue (RGB) image via an RGB image-capturing device. Another step involves registering the depth map image and the RGB image to a common coordinate system. Yet another step involves projecting the at least one virtual construction jobsite object in a scene of the construction jobsite with the use of geographical information system (GIS) data, computer-aided design (CAD) data, or both types of data, to generate the augmented reality scene of the construction jobsite. And yet another step involves removing hidden surfaces of the at least one virtual construction jobsite object in the augmented reality scene.

Abstract: A method and system of estimating the three-dimensional (3D) position and orientation (pose) of an articulated machine, such as an excavator, involves the use of one or more marker(s) and one or more image-capturing device(s). Images of the marker(s) are captured via the image-capturing device(s), and the captured images are used to determine pose. Furthermore, the pose of non-articulated components of the articulated machine, of articulated components of the articulated machine, and of end effectors of the articulated machine can all be estimated with the method and system.

Abstract: A method and system of monitoring proximity of objects at a construction jobsite via three-dimensional virtuality in real-time. The method and system involves simulating a dynamic object such as a piece of construction equipment in a three-dimensional virtual representation of the construction jobsite, and involves simulating another object such as a buried utility or another piece of construction equipment in the three-dimensional virtual representation of the construction jobsite. The method and system also involves making one or more determinations about the objects in order to more readily avoid unintended impact between them.