公开(公告)号：US20220406005A1

公开(公告)日：2022-12-22

申请号：US17702982

申请日：2022-03-24

Applicant: FARO Technologies, Inc.

Inventor： Marco Lombardi ,  Francesco Bonarrigo ,  Andrea Riccardi ,  Federico Barone

IPC: G06T15/20 ,  G06T7/593 ,  G06T7/73 ,  G06V10/10

Abstract: Technical solutions are described to track a handheld three-dimensional (3D) scanner in an environment using natural features in the environment. In one or more examples, the natural features are detected using machine learning. Features are filtered by performing a stereo matching between respective pairs of stereo images captured by the scanner. The features are further filtered using time matching between images captured by the scanner at different timepoints.

公开(公告)号：US12086925B2

公开(公告)日：2024-09-10

申请号：US17702982

申请日：2022-03-24

Applicant: FARO Technologies, Inc.

Inventor： Marco Lombardi ,  Francesco Bonarrigo ,  Andrea Riccardi ,  Federico Barone

IPC: G06T15/20 ,  G06T7/593 ,  G06T7/73 ,  G06V10/10

CPC classification number: G06T15/205 ,  G06T7/593 ,  G06T7/73 ,  G06V10/17 ,  G06T2207/10012 ,  G06T2207/10028 ,  G06T2207/20084

Abstract: Technical solutions are described to track a handheld three-dimensional (3D) scanner in an environment using natural features in the environment. In one or more examples, the natural features are detected using machine learning. Features are filtered by performing a stereo matching between respective pairs of stereo images captured by the scanner. The features are further filtered using time matching between images captured by the scanner at different timepoints.

公开(公告)号：US12047550B2

公开(公告)日：2024-07-23

申请号：US17695352

申请日：2022-03-15

Applicant: FARO Technologies, Inc.

Inventor： Georgios Balatzis ,  Francesco Bonarrigo ,  Andrea Riccardi

IPC: H04N13/271 ,  G03B35/12 ,  G06T7/521 ,  G06T7/593 ,  G06T7/80 ,  H04N13/239 ,  H04N13/246 ,  H04N13/254 ,  H04N23/90 ,  H04N13/00

CPC classification number: H04N13/271 ,  G03B35/12 ,  G06T7/521 ,  G06T7/593 ,  G06T7/85 ,  H04N13/239 ,  H04N13/246 ,  H04N13/254 ,  H04N23/90 ,  G06T2200/04 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20228 ,  H04N2013/0081

Abstract: An example method for training a machine learning model is provided. The method includes receiving training data collected by a three-dimensional (3D) imager, the training data comprising a plurality of training sets. The method further includes generating, using the training data, a machine learning model from which a disparity map can be inferred from a pair of images that capture a scene where a light pattern is projected onto an object.

公开(公告)号：US11908162B2

公开(公告)日：2024-02-20

申请号：US17556083

申请日：2021-12-20

Applicant: FARO Technologies, Inc.

Inventor： Francesco Bonarrigo ,  Paul C. Atwell ,  John Lucas Creachbaum ,  Nitesh Dhasmana ,  Fabiano Kovalski ,  Andrea Riccardi ,  William E. Schoenfeldt ,  Marco Torsello ,  Christopher Michael Wilson

IPC: G06T7/73 ,  G01C11/02 ,  H04N23/55 ,  H04N23/56 ,  H04N23/80 ,  H04N13/239

CPC classification number: G06T7/74 ,  G01C11/02 ,  H04N23/55 ,  H04N23/56 ,  H04N23/80 ,  G06T2207/10012 ,  G06T2207/30204 ,  H04N13/239

Abstract: A handheld three-dimensional (3D) measuring system operates in a target mode and a geometry mode. In the target mode, a target-mode projector projects a first line of light onto an object, and a first illuminator sends light to markers on or near the object. A first camera captures an image of the first line of light and the illuminated markers. In the geometry mode, a geometry-mode projector projects onto the object a first multiplicity of lines, which are captured by the first camera and a second camera. One or more processors determines 3D coordinates in the target mode and the geometry mode.