Autodesk, Inc.

Facebook Technologies, LLC

Oytun Akman

Ronald Poelman

Seth Koterba

Ananth Ranganathan

Yan Fu

Jeffrey Witthuhn

Ioannis Pavlidis

Meghana Reddy Guduru

David Timothy Rudolf

A method, system, and apparatus create a 3D CAD model. Scan data from two or more structured scans of a real-world scene are acquired and each scan processed independently by segmenting the scan data into multiple segments, filtering the scan data, and fitting an initial model that is used as a model candidate. Model candidates are clustered into groups and a refined model is fit onto the model candidates in the same group. A grid of cells representing points is mapped over the refined model. Each of the grid cells is labeled by processing each scan independently, labeling each cell located within the refined model as occupied, utilizing back projection to label remaining cells as occluded or empty. The labels from multiple scans are then combined. Based on the labeling, model details are extracted to further define and complete the refined model.

Three-dimensional (3D) model creation and incremental model refinement from laser scans

In general, this disclosure describes an artificial reality system that provides asymmetric user experiences to users associated with user devices that operate according to different modes of engagement with the artificial reality system. Different user devices may have different capabilities, be used by users having different roles for an artificial reality application, or otherwise be configured to interact in a variety of ways with an artificial reality system.

Artificial reality system having multiple modes of engagement

A method, system, apparatus, article of manufacture, and computer-readable storage medium provide the ability to merge multiple point cloud scans. A first raw scan file and a second raw scan file (each including multiple points) are imported. The scan files are segmented by extracting segments based on geometry in the scene. The segments are filtered. A set of candidate matching feature pairs are acquired by registering features from one scan to features from another scan. The two raw scan files are merged based on the candidate matching feature pairs.

Method and system for merging multiple point cloud scans

A method, apparatus, and system provides the ability to process and render a point cloud. The points in the point cloud are grouped into three-dimensional (D) voxels. A position of each of the points is stored in the point data file. The position is with respect to a location of the point's correspondingD voxel. Surface normal data for a surface normal associated with each of the points is also stored in the point data file. The points are organized into levels of details (LODs). The point data file is provided to a graphics processing unit (GPU) that processes the point data file to render the point cloud. During rendering, a LOD is selected to determine the points in the point cloud to render.

Out-of-core point rendering with dynamic shapes

A method, apparatus, system, and computer readable storage medium provide the ability to pre-segment point cloud data. Point cloud data is obtained and segmented. Based on the segment information, a determination is made regarding points needed for shape extraction. Needed points are fetched and used to extract shapes. The extracted shapes are used to cull points from the point cloud data.

Pre-segment point cloud data to run real-time shape extraction faster

A method, system, apparatus, article of manufacture, and computer-readable storage medium provide the ability to merge multiple point cloud scans. A first raw scan file and a second raw scan file (each including multiple points) are imported. The scan files are segmented by extracting segments based on geometry in the scene. The segments are filtered to reduce a number of segments and identify features. A set of candidate matching feature pairs are acquired by coarsely registering features from one scan to features from another scan. The candidate pairs are refined by improving alignment based on corresponding points in the features. The candidate pairs are scored and then merged based on the scores.

Automatic registration

A method, system, apparatus, article of manufacture, and computer-readable storage medium provide the ability to merge multiple point cloud scans. A first raw scan file and a second raw scan file (each including multiple points) are imported. The scan files are segmented by extracting segments. Features are extracted from the segments. A set of candidate matching feature pairs are acquired by registering/matching/pairing features from one scan to features from another scan. The candidate pairs are refined based on an evaluation of all of the matching pairs. The candidate pairs are further refined by extracting sample points from the segments (within the matched pairs) and refining the pairs based on the points. The feature pairs are scored and then merged based on the scores.

A method, apparatus, system, and computer readable storage medium provide the ability to pre-segment point cloud data. Point cloud data is obtained and segmented. The segment information is stored. An indexing structure is created and instantiated with the point cloud data and the segment information. Based on the segment information, a determination is made regarding points needed for shape extraction. Needed points are fetched from the indexing structure an used to extract shapes. The extracted shapes are used to cull points from the point cloud data.

Growing community of inventors

Kensington, CA, United States of America

Oytun Akman