Faro Technologies, Inc.

Jafar Amiri Parian

Hani Kaabi

Robert E Bridges

Martin Ossig

Matthias Wolke

A method includes capturing, by a three-dimensional (3D) scanner, a 3D point cloud, and capturing, by a camera, a control image by capturing and stitching multiple images of the surrounding environment. The method further includes capturing, by an auxiliary camera, an ultrawide-angle calibration image. The method further includes dynamically calibrating the auxiliary camera using the 3D point cloud, the control image, and the calibration image. The calibrating includes extracting a first plurality of features from the control image and extracting a second plurality of features from the calibration image. Further, a set of matching features are determined from the first and second sets of features. A set of control points is generated using the set of matching features by determining points in the 3D point cloud that correspond to the set of matching features. Further, a self-calibration of the auxiliary camera is performed using the set of control points.

Dynamic self-calibrating of auxiliary camera of laser scanner

Examples described herein provide a method that includes capturing data about an environment. The method further includes generating a database of two-dimensional (2D) features and associated three-dimensional (3D) coordinates based at least in part on the data about the environment. The method further includes determining a position (x, y, z) and an orientation (pitch, roll, yaw) of a device within the environment based at least in part on the database of 2D features and associated 3D coordinates. The method further includes causing the device to display, on a display of the device, an augmented reality element at a predetermined location based at least in part on the position and the orientation of the device.

Tracking with reference to a world coordinate system

A computer-implemented method is performed by one or more processors to automatically register a plurality of captured data obtained using a respective measurement device, each of the captured data is obtained separately. The computer-implemented method includes accessing a first captured data of a portion of an environment, and a first image corresponding to said portion of the environment captured from a known relative position and angle with respect to the first captured data. Further, from the plurality of captured data, a second captured data is identified that has at least a partial overlap with said portion, the second captured data is identified based on a corresponding second image. The second image is captured from a known relative position and angle with respect to the second captured data. The method further includes transforming the second captured data and/or the first captured data to a coordinate system.

Automatic registration of multiple measurement devices

According to one or more embodiments, a method includes capturing a first three-dimensional (3D) point cloud and a second 3D point cloud. Each of the 3D point clouds includes a plurality 3D coordinates corresponding to one or more objects scanned in a surrounding environment. The first 3D point cloud and the second 3D point cloud capturing at least one overlapping portion. Further, the method includes capturing a first ultrawide-angle image and a second ultrawide-angle image of the surrounding environment, the first ultrawide-angle image captures color information of the first 3D point cloud, and the second ultrawide-angle image captures color information of the second 3D point cloud. The method further includes registering the first 3D point cloud and the second 3D point cloud by mapping one or more features from the first ultrawide-angle image and the second ultrawide-angle image.

Laser scanner with ultrawide-angle lens camera for registration

A system includes a first type of measurement device that captures first 2D images, a second type of measurement device that captures 3D scans. A 3D scan includes a point cloud and a second 2D image. The system also includes processors that register the first 2D images. The method includes accessing the 3D scan that records at least a portion of the surrounding environment that is also captured by a first 2D image. Further, 2D features in the second 2D image are detected, and 3D coordinates from the point cloud are associated to the 2D features. 2D features are also detected in the first 2D image, and matching 2D features from the first 2D image and the second 2D image are identified. A position and orientation of the first 2D image is calculated in a coordinate system of the 3D scan using the matching 2D features.

Markerless registration of image and laser scan data

A point cloud is colorized by mapping a color image using an intensity image. The mapping includes detecting multiple features from the intensity image using a feature-extraction algorithm. A feature is extracted that is not within a predetermined vicinity of an edge in the intensity image. A template is created by selecting a portion of a predetermined size from the intensity image with the feature at the center. A search window is created with the same size as the template by selecting a portion of a luminance image as a search space. The luminance image is obtained from the color image. A cost value is computed for each pixel of the search space by comparing image gradients of the template and the search window. A matching point is determined in the color image corresponding to the feature based on the cost value for each pixel of search space.

Hybrid feature matching between intensity image and color image

A system includes a three-dimensional (3D) scanner, a camera with a viewpoint that is different from a viewpoint of the 3D scanner, and one or more processors coupled with the 3D scanner and the camera. The processors access a point cloud from the 3D scanner and one or more images from the camera, the point cloud comprises a plurality of 3D scan-points, a 3D scan-point represents a distance of a point in a surrounding environment from the 3D scanner, and an image comprises a plurality of pixels, a pixel represents a color of a point in the surrounding environment. The processors generate, using the point cloud and the one or more images, an artificial image that represents a portion of the surrounding environment viewed from an arbitrary position in an arbitrary direction, wherein generating the artificial image comprises colorizing each pixel in the artificial image.

Artificial panorama image production and in-painting for occluded areas in images

A system includes a three-dimensional (3D) scanner that captures a 3D point cloud corresponding to one or more objects in a surrounding environment. The system further includes a camera that captures a control image by capturing a plurality of images of the surrounding environment, and an auxiliary camera configured to capture an ultrawide-angle image of the surrounding environment. One or more processors of the system colorize the 3D point cloud using the ultrawide-angle image by mapping the ultrawide-angle image to the 3D point cloud. The system performs a limited system calibration before colorizing each 3D point cloud, and a periodic full system calibration before/after a plurality of 3D point clouds are colorized.

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Schlieren, Switzerland

Jafar Amiri Parian