Bentley Systems, Inc.

Cyril Novel

Alexandre Gbaguidi Aisse

Luc Robert

Jean-Philippe Pons

A sample may be generated for each point of a plurality of point clouds that represent a scene. A visibility ray may be created between each point of the plurality of point clouds and the one or more sources that generated the point. One or more sample, if any, that intersect a visibility ray may be identified. Each point corresponding to an intersecting sample may be determined to represent or likely represent an unwanted object if the visibility ray is from a different source that did not generate the point and the point is not coherent with any points generated by the different source. A visibility score for each point determined to represent or likely represent an unwanted object may be adjusted. A model may be generated, wherein the model does not include the unwanted object in the scene but includes the permanent object with see-through characteristics in the scene.

Systems, methods, and media for filtering points of a point cloud utilizing visibility factors to generate a model of a scene

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.

Assigning each point of a point cloud to a scanner position of a plurality of different scanner positions in a point cloud

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.

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Cyril Novel