US 12,450,872 B2
Machine learning techniques for ground classification
Kevin Balkoski, San Mateo, CA (US); Edward Melcher, Toronto (CA); Eleanor Crane, Sunnyvale, CA (US); and Matthieu Francois Perrinel, Berkeley, CA (US)
Assigned to CoStar Realty Information, Inc., Arlington, VA (US)
Filed by Matterport, Inc., Sunnyvale, CA (US)
Filed on Oct. 6, 2022, as Appl. No. 17/938,547.
Claims priority of provisional application 63/252,873, filed on Oct. 6, 2021.
Prior Publication US 2023/0104674 A1, Apr. 6, 2023
Int. Cl. G06V 10/764 (2022.01); G01S 17/89 (2020.01); G06V 10/26 (2022.01); G06V 10/40 (2022.01); G06V 10/774 (2022.01); G06V 20/17 (2022.01)
CPC G06V 10/764 (2022.01) [G01S 17/89 (2013.01); G06V 10/267 (2022.01); G06V 10/40 (2022.01); G06V 10/774 (2022.01); G06V 20/17 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
obtaining a point cloud that represents an environment based at least in part on a plurality of points in three-dimensional space;
determining corresponding classifications of points in the point cloud as ground or not-ground based at least in part on a plurality of ground classification algorithms;
determining respective point cloud features associated with the points in the point cloud;
determining respective cell features associated with a plurality of cells that segment the point cloud;
generating feature data for a machine learning model based on at least two of: the classifications of the points based on the plurality of ground classification algorithms, the point cloud features, or the cell features; and
classifying the points in the point cloud based at least in part on an output from the machine learning model in response to input of the feature data.