Abstract: In an example embodiment, a process may create a disk for each point of the point cloud, where a size of the disk and the orientation of the disk are respectively based on a computed scale and a non-oriented normal. The process may insert each disk into a search structure that is queried to determine if one or more disks intersect the paths from a given point to each scanner in the point cloud. The process may create an output corresponding to each scanner in the point cloud, where each output includes the number of intersecting disks and the distance from the given point to the scanner. The process may implement a sorting algorithm to assign the given point to a scanner position. An application may utilize the assignment of each point in the point cloud to a scanner position to generate a high-resolution 3D mesh of a scene.

Abstract: In an example embodiment, a process may select high density points from a point cloud. The process may create one or more clusters from the high density points and identify a circular cluster from the created clusters. The process may identify which points in the circular cluster are inner edge points and determine a center of an ellipse that fits the inner edge points. The process may define a search space utilizing the center of the ellipse. The process may determine the estimated x, y, and z coordinates for the position of the scanner in the search space utilizing a non-linear least square solver with different combinations of a relationship that is true for any pair of points of the cluster. An application may utilize the determined position with an object/file format (e.g., LSA format) to generate a high resolution 3D mesh of a scene.

Abstract: In an example embodiment, a process may create a disk for each point of the point cloud, where a size of the disk and the orientation of the disk are respectively based on a computed scale and a non-oriented normal. The process may insert each disk into a search structure that is queried to determine if one or more disks intersect the paths from a given point to each scanner in the point cloud. The process may create an output corresponding to each scanner in the point cloud, where each output includes the number of intersecting disks and the distance from the given point to the scanner. The process may implement a sorting algorithm to assign the given point to a scanner position. An application may utilize the assignment of each point in the point cloud to a scanner position to generate a high-resolution 3D mesh of a scene.

Abstract: In an example embodiment, a process may select high density points from a point cloud. The process may create one or more clusters from the high density points and identify a circular cluster from the created clusters. The process may identify which points in the circular cluster are inner edge points and determine a center of an ellipse that fits the inner edge points. The process may define a search space utilizing the center of the ellipse. The process may determine the estimated x, y, and z coordinates for the position of the scanner in the search space utilizing a non-linear least square solver with different combinations of a relationship that is true for any pair of points of the cluster. An application may utilize the determined position with an object/file format (e.g., LSA format) to generate a high resolution 3D mesh of a scene.