Faro Technologies, Inc.

Louis Bergmann

Daniel Flohr

Manuel Caputo

Oliver Zweigle

Daniel Pompe

Vadim Demkiv

José Gerardo Gómez Méndez

Zia Ul Azam

Jörg Braunes

A virtual reality (VR) system that includes a three-dimensional (3D) point cloud having a plurality of points, a VR viewer having a current position, a graphics processing unit (GPU), and a central processing unit (CPU). The CPU determines a field-of-view (FOV) based at least in part on the current position of the VR viewer, selects, using occlusion culling, a subset of the points based at least in part on the FOV, and provides them to the GPU. The GPU receives the subset of the plurality of points from the CPU and renders an image for display on the VR viewer based at least in part on the received subset of the plurality of points. The selecting a subset of the plurality of points is at a first frame per second (FPS) rate and the rendering is at a second FPS rate that is faster than the first FPS rate.

Virtual reality system for viewing point cloud volumes while maintaining a high point cloud graphical resolution

An example method for training a neural network includes generating a training data set of point clouds. The training data set includes pairs of closed surfaces point clouds and non-closed surfaces point clouds. The method further includes, for each of the closed surface point clouds and the non-closed surface point clouds, generating a two-dimensional (2D) image by rendering a three-dimensional scene. The 2D image for the non-closed surfaces point clouds includes a gap in a surface, and the 2D image for the closed surfaces point clouds are free of gaps. The method further includes training the neural network to generate a trained neural network. The method further includes filling, using the trained neural network, gaps between scan points of the 2D image, and de-noising, using the trained neural network, scan point cloud data to generate a closed surface image of the scan point cloud data.

System and method for training a neural network to fill gaps between scan points in images and to de-noise point cloud images

A system and a method for removing artifacts from a 3D coordinate data are provided. The system includes one or more processors and a measuring device. The one or more processors are operable to receive training data and train the 3D measuring device to identify artifacts by analyzing the training data. The one or more processors are further operable to identify artifacts in live data based on the training of the processor system. The one or more processors are further operable to generate clear scan data by filtering the artifacts from the live data and output the clear scan data.

Artifact filtering using artificial intelligence

A system of generating a three-dimensional (3D) scan of an environment includes multiple 3D scanners including a first 3D scanner at respective first and second positions. The system further includes a controller coupled to the 3D scanners via a common communications network. The first scanner and second scanner transmit a subset of data to the controller while acquiring a set of 3D coordinates. The controller registers the subsets of data to each other while the sets of 3D coordinates is being acquired.

System and method of registering point cloud data using subsample data

Provided are embodiments for a method for performing colorization of scans. The method includes analyzing a scanner, a scan of an environment to identify one or more patterns within the scan, and obtaining a subset of colorization data of the environment. The method also includes predicting colors for the one or more patterns in the scan based on the subset of colorization data, and assigning the predicted colors to the one or more patterns in the scan to generate a colorized scan. The method includes displaying the colorized scan, wherein the colorized scan combines the scan and the predicted colorization data by assigning the predicted colorization data to the one or more patterns in the scan. Also provided are embodiments for a system for performing the colorization of scans.

Artificial intelligence scan colorization

A system of generating a three-dimensional (3D) scan of an environment includes multiple 3D scanners including a first 3D scanner at respective first and second positions. The system further includes a controller coupled to the 3D scanners. The first 3D scanner acquires a first set of 3D coordinates, the first set of 3D coordinates having a first portion. The second 3D scanner acquires a second set of 3D coordinates, the second set of 3D coordinates having a second portion. The first portion and the second portion are simultaneously transmitted to the controller by the first 3D scanner and the second 3D scanner respectively, while the first set of 3D coordinates and the second set of 3D coordinates are being acquired. The controller registers the first portion and the second portion to each other while the first set of 3D coordinates and the second set of 3D coordinates are being acquired.

System and method of scanning an environment using multiple scanners concurrently

A collaborative virtual reality (VR) online meeting platform that includes a three-dimensional (3D) point cloud data defining a virtual space, identifiers of a plurality of meeting participants, and meeting data that includes positions in the virtual space of a plurality of avatars that correspond to meeting participants. The system includes a processor that includes instructions to initiate an online meeting of the plurality of meeting participants. The initiating includes providing an address of the 3D point cloud to each of the meeting participants and transmitting a copy of the 3D point cloud data and meeting data to each of the meeting participants. A current location of each of the avatars in the virtual space is communicated to all of the meeting participants.

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Stuttgart, Germany

Louis Bergmann