Abstract: In one embodiment, a method includes accessing time series data of a wireless communication link between a first node and a second node on a millimeter-wave communications network; determining that an anomaly exists in the time series data; determining an anomaly type for the anomaly by inputting one or more features derived from the time series data into a machine learning module; generating a report comprising an indication that the anomaly exists and a description of the anomaly type, and determining one or more treatments for the determined anomaly.

Abstract: In one embodiment, a method includes accessing a point cloud comprising a plurality of point-cloud points, each point-cloud point corresponding to a location on a surface of an object located in a region in a three-dimensional space, identifying, from the point cloud, a plurality of point clusters, each point cluster comprising a plurality of point-cloud points located within a grid segment on a two-dimensional grid derived from the three-dimensional space, selecting, for each point cluster, a set of point-cloud points from the plurality of point-cloud points in the point cluster, the set of point-cloud points being selected based on a predetermined threshold number of point-cloud points associated with an acceptable reduction in an error detection rate, and determining, for each point cluster, a structure classification based on the selected set of point-cloud points from the point cluster.

Abstract: In one embodiment, a method includes accessing a point cloud comprising a plurality of point-cloud points, each point-cloud point corresponding to a location on a surface of an object located in a region in a three-dimensional space, identifying, from the point cloud, a plurality of point clusters, each point cluster comprising a plurality of point-cloud points located within a grid segment on a two-dimensional grid derived from the three-dimensional space, selecting, for each point cluster, a set of point-cloud points from the plurality of point-cloud points in the point cluster, the set of point-cloud points being selected based on a predetermined threshold number of point-cloud points associated with an acceptable reduction in an error detection rate, and determining, for each point cluster, a structure classification based on the selected set of point-cloud points from the point cluster.

Abstract: In one embodiment, a method includes accessing a point cloud comprising several points, wherein each point corresponds to a location on a surface of an object located in three-dimensional space; determining whether each point in the point cloud is part of a linear structure, a planar structure, or a volumetric structure; identifying a plurality of point clusters, wherein each point cluster comprises one or more points that are located within a grid segment on a two-dimensional grid derived from the three-dimensional space; determining, for each point cluster, whether the point cluster represents a vertical-linear structure or a portion of a vertical-linear structure; identifying one or more point-cluster pairs, wherein each point-cluster pair includes two point clusters corresponding to one or more vertical-linear structures within a threshold distance in the three-dimensional space; and determining, for each point-cluster pair, whether a line-of-sight exists between each point-cluster in the point-cluster pa

Abstract: In one embodiment, a method includes accessing a point cloud comprising several points, wherein each point corresponds to a location on a surface of an object located in three-dimensional space; determining whether each point in the point cloud is part of a linear structure, a planar structure, or a volumetric structure; identifying a plurality of point clusters, wherein each point cluster comprises one or more points that are located within a grid segment on a two-dimensional grid derived from the three-dimensional space; determining, for each point cluster, whether the point cluster represents a vertical-linear structure or a portion of a vertical-linear structure; identifying one or more point-cluster pairs, wherein each point-cluster pair includes two point clusters corresponding to one or more vertical-linear structures within a threshold distance in the three-dimensional space; and determining, for each point-cluster pair, whether a line-of-sight exists between each point-cluster in the point-cluster pa

Abstract: A method of detecting objects in three-dimensional (3D) point clouds and detecting differences between 3D point clouds and the objects therein is disclosed. A method includes receiving a first scene 3D point cloud and a second scene 3D point cloud, wherein the first scene 3D point cloud and the second scene 3D point cloud include first and second target objects, respectively; aligning the first scene 3D point cloud and the second scene 3D point cloud; detecting the first and second target objects from the first scene 3D point cloud and the second scene 3D point cloud, respectively; comparing the detected first target object with the detected second target object; and identifying, based on the comparison, one or more differences between the detected first target object and the detected second target object. Further aspects relate to detecting changes of target objects within scenes of multiple 3D point clouds.

Abstract: A scene point cloud is processed and a solution to an inverse-function is determined to determine its source objects. A primitive extraction process and a part matching process are used to compute the inverse function solution. The extraction process estimates models and parameters based on evidence of cylinder and planar geometry in the scene. The matching process matches clusters of 3D points to models of parts from a library. A selected part and its associated polygon model is used to represent the point cluster. Iterations of the extraction and matching processes complete a 3D model for a complex scene made up of planes, cylinders, and complex parts from the parts library. Connecting regions between primitives and/or parts are processed to determine their existence and type. Constraints may be used to ensure a connected model and alignment of its components.

Abstract: A method of detecting objects in three-dimensional (3D) point clouds and detecting differences between 3D point clouds and the objects therein is disclosed. A method includes receiving a first scene 3D point cloud and a second scene 3D point cloud, wherein the first scene 3D point cloud and the second scene 3D point cloud include first and second target objects, respectively; aligning the first scene 3D point cloud and the second scene 3D point cloud; detecting the first and second target objects from the first scene 3D point cloud and the second scene 3D point cloud, respectively; comparing the detected first target object with the detected second target object; and identifying, based on the comparison, one or more differences between the detected first target object and the detected second target object. Further aspects relate to detecting changes of target objects within scenes of multiple 3D point clouds.

Abstract: A scene point cloud is processed and a solution to an inverse-function is determined to determine its source objects. A primitive extraction process and a part matching process are used to compute the inverse function solution. The extraction process estimates models and parameters based on evidence of cylinder and planar geometry in the scene. The matching process matches clusters of 3D points to models of parts from a library. A selected part and its associated polygon model is used to represent the point cluster. Iterations of the extraction and matching processes complete a 3D model for a complex scene made up of planes, cylinders, and complex parts from the parts library. Connecting regions between primitives and/or parts are processed to determine their existence and type. Constraints may be used to ensure a connected model and alignment of its components.