Faro Technologies, Inc.

Francesco Bonarrigo

Andrea Riccardi

Paul Christopher Atwell

John Lucas Creachbaum

Nitesh Dhasmana

Christopher Michael Wilson

Georgios Balatzis

William E Schoenfeldt

Fabiano Kovalski

Marco Lombardi

Federico Barone

Marco Torsello

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.

Targetless tracking of measurement device during capture of surrounding data

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.

Three-dimiensional point cloud generation using machine learning

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.

Line scanner having target-tracking and geometry-tracking modes

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Ghedi, Italy

Francesco Bonarrigo