Qualcomm Incorporated

Hilti Aktiengesellschaft

Qualcomm Technologies, Inc.

Ar4 Gmbh

Clemens Arth

Dieter Schmalstieg

Vincent Lepetit

Gerhard Reitmayr

Christian Pirchheim

Alessandro Mulloni

Paul Wohlhart

Peter Michael Roth

Denis Kalkofen

Thomas Gloor

Shreyas Hampali

Philipp Fleck

Peter Mohr

Martin Hirzer

Christoph Klug

A method for registering a total station, which is deployed in a measurement environment with one or more objects, in the reference system of a computer-aided design (CAD) model, which images the one or more objects of the measurement environment, includes registering the total station by a control device.

Method for registering a total station in the reference system of a CAD model

Systems and techniques are provided for determining one or more poses of one or more objects. For example, a process can include determining, using a machine learning system, a plurality of keypoints from an image. The plurality of keypoints are associated with at least one object in the image. The process can include determining a plurality of features from the machine learning system based on the plurality of keypoints. The process can include classifying the plurality of features into a plurality of joint types. The process can include determining pose parameters for the at least one object based on the plurality of joint types.

Keypoint-based sampling for pose estimation

A method determines a pose of an image capture device. The method includes accessing an image of a scene captured by the image capture device. A semantic segmentation of the image is performed, to generate a segmented image. An initial pose of the image capture device is generated using a three-dimensional (3D) tracker. A plurality of 3D renderings of the scene are generated, each of the plurality of 3D renderings corresponding to one of a plurality of poses chosen based on the initial pose. A pose is selected from the plurality of poses, such that the 3D rendering corresponding to the selected pose aligns with the segmented image.

Pose determination with semantic segmentation

Disclosed are a system, apparatus, and method for multiple client simultaneous localization and mapping. Tracking and mapping may be performed locally and independently by each of a plurality of clients. At configurable points in time map data may be sent to a server for stitching and fusion. In response to successful stitching and fusion to one or more maps known to the server, updated position and orientation information relative to the server's maps may be sent back to the clients. Clients may update their local map data with the received server location data. Clients may receive additional map data from the server, which can be used for extending their maps. Clients may send queries to the server for 3D maps, and the queries may include metadata.

Scalable 3D mapping system

For an image processing concept, a database including data derived from a plurality of frames of a video is provided in a device. A live video feed is obtained from a camera of the device. Information is extracted from an image of the video feed. A search is performed in the database using the extracted information to retrieve a list of potential frames out of the plurality of frames. An initial pose of the selected image is estimated with respect to one frame of the list as a function of the extracted information and the data derived from the one frame. Respective subsequent poses for subsequent images from the live video feed are iteratively estimated, wherein the associated subsequent pose is estimated based on said subsequent image and a respective previously estimated pose. The video feed is augmented on the device with virtual information based on the estimated initial pose and the subsequent poses.

Image processing method, mobile device and method for generating a video image database

A computer-implemented method, apparatus, computer readable medium and mobile device for determining a 6DOF pose from an input image. The process of determining 6DOF pose may include processing an input image to create one or more static representations of the input image, creating a dynamic representation of the input image from an estimated 6DOF pose and a 2.5D reference map, and measuring correlation between the dynamic representation and the one or more static representations of the input image. The estimated 6DOF may be iteratively adjusted according to the measured correlation error until a final adjusted dynamic representation meets an output threshold.

Predictor-corrector based pose detection

Techniques are presented for monocular visual simultaneous localization and mapping (SLAM) based on detecting a translational motion in the movement of the camera using at least one motion sensor, while the camera is performing panoramic SLAM, and initializing a three dimensional map for tracking of finite features. Motion sensors may include one or more sensors, including inertial (gyroscope, accelerometer), magnetic (compass), vision (camera) or any other sensors built into mobile devices.

Sensor-based camera motion detection for unconstrained slam

A mobile device uses vision and orientation sensor data jointly for six degree of freedom localization, e.g., in wide-area environments. An image or video stream is captured while receiving geographic orientation data and may be used to generate a panoramic cylindrical map of an environment. A bin of model features stored in a database is accessed based on the geographic orientation data. The model features are from a pre-generated reconstruction of the environment produced from extracted features from a plurality of images of the environment. The reconstruction is registered to a global orientation and the model features are stored in bins based on similar geographic orientations. Features from the panoramic cylindrical map are matched to model features in the bin to produce a set of corresponding features, which are used to determine a position and an orientation of the camera.

Sensor-aided wide-area localization on mobile devices

Pose estimation is performed using a scene structure captured in a query image and reference images from a database. Each of the reference images has an associated position estimate. Direction vectors are generated that describe directions between an unknown position of a camera center for the query image and a reference camera center for each reference image based on the query image and the plurality of reference images. The direction vectors may be generated using, e.g., homographies, essential matrices, or fundamental matrices. A pose estimation with six degrees of freedom is determined using the direction vectors and the associated position estimate for each reference image. The pose estimation, for example, may be determined by solving a three-point pose problem using the direction vectors and the associated position estimate for each reference image.

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Clemens Arth