Apple Inc.

Peter Georg Meier

Daniel R Kurz

Sawyer Isaac Cohen

Benjamin B Lyon

Mark Rober

Michael Kuhn

Tanmay Batra

Frank Angermann

Holly Gerhard

Jeffrey M Riepling

Patrick Steven Piemonte

Ian M Richter

Daniel Ulbricht

Michael J Rockwell

Sebastian Knorr

Tobias Holl

Daniel De Rocha Rosario

Jason D Gosnell

Sebastian Lieberknecht

Omar Elafifi

Christian Lipski

Sebastian Bernhard Knorr

Jean-Daniel E Nahmias

A method for representing virtual information in a view of a real environment comprises providing a virtual object having a global position and orientation with respect to a geographic global coordinate system, with first pose data on the global position and orientation of the virtual object, in a database of a server, taking an image of a real environment by a mobile device and providing second pose data as to at which position and with which orientation with respect to the geographic global coordinate system the image was taken. The method further includes displaying the image on a display of the mobile device, accessing the virtual object in the database and positioning the virtual object in the image on the basis of the first and second pose data, manipulating the virtual object or adding a further virtual object, and providing the manipulated virtual object with modified first pose data or the further virtual object with third pose data in the database.

Method for representing virtual information in a view of a real environment

An exemplary process for identifying a type of a physical environment amongst a plurality of types of physical environments is provided. The process includes obtaining, using the one or more cameras, image data corresponding to a physical environment. The process further includes identifying at least one portion of an entity in the physical environment based on the image data; determining, based on the identified at least one portion of the entity, whether the entity is an entity of a first type; determining a type of the physical environment if the entity is an entity of the first type; and presenting one or more virtual objects and a representation of the entity.

Presenting virtual objects in a physical environment

A VR system for vehicles that may implement methods that address problems with vehicles in motion that may result in motion sickness for passengers. The VR system may provide virtual views that match visual cues with the physical motions that a passenger experiences. The VR system may provide immersive VR experiences by replacing the view of the real world with virtual environments. Active vehicle systems and/or vehicle control systems may be integrated with the VR system to provide physical effects with the virtual experiences. The virtual environments may be altered to accommodate a passenger upon determining that the passenger is prone to or is exhibiting signs of motion sickness.

Immersive virtual display

In one implementation, a method includes: obtaining a user input to view SR content associated with video content; if the video content includes a first scene when the user input was detected: obtaining first SR content for a first time period of the video content associated with the first scene; obtaining a task associated with the first scene; and causing presentation of the first SR content and a first indication of the task associated with the first scene; and if the video content includes a second scene when the user input was detected: obtaining second SR content for a second time period of the video content associated with the second scene; obtaining a task associated with the second scene; and causing presentation of the second SR content and a second indication of the task associated with the second scene.

Method and device for presenting synthesized reality companion content

In some implementations initially training a first neural network includes inputting the training inputs and corresponding training labels into the first neural network to produce output labels, comparing the output labels to the corresponding training labels using a second neural network that learns and applies a comparison metric, and adjusting parameters of the first neural network based on the comparing. The device then inputs additional inputs into the first neural network to produce additional output labels and corresponding confidence values from the second neural network. The device selects, based on the confidence values, an automatically-labeled training set of data including a subset of the additional inputs and a corresponding subset of the additional output labels. During a second training stage, the device trains the first neural network and the second neural network using the automatically-labeled training set of data.

Iterative neural network training using quality assurance neural network

A method of tracking a mobile device comprising at least one camera in a real environment comprises the steps of receiving image information associated with at least one image captured by the at least one camera, generating a first geometrical model of at least part of the real environment based on environmental data or mobile system state data acquired in an acquisition process by at least one sensor of a mobile system, which is different from the mobile device, and performing a tracking process based on the image information associated with the at least one image and at least partially according to the first geometrical model, wherein the tracking process determines at least one parameter of a pose of the mobile device relative to the real environment. The invention is also related to a method of generating a geometrical model of at least part of a real environment using image information from at least one camera of a mobile device.

Method of tracking a mobile device and method of generating a geometrical model of a real environment using a camera of a mobile device

In some implementations a neural network is trained to perform a main task using a clustering constraint, for example, using both a main task training loss and a clustering training loss. Training inputs are inputted into a main task neural network to produce output labels predicting locations of the parts of the objects in the training inputs. Data from pooled layers of the main task neural network is inputted into a clustering neural network. The main task neural network and the clustering neural network are trained based on a main task loss from the main task neural network and a clustering loss from the clustering neural network. The main task loss is determined by comparing differences between the output labels and the training labels. The clustering loss encourages the clustering network to learn to label the parts of the objects individually, e.g., to learn groups corresponding to the object parts.

Semi-supervised learning using clustering as an additional constraint

An AR system that leverages a pre-generated 3D model of the world to improve rendering of 3D graphics content for AR views of a scene, for example an AR view of the world in front of a moving vehicle. By leveraging the pre-generated 3D model, the AR system may use a variety of techniques to enhance the rendering capabilities of the system. The AR system may obtain pre-generated 3D data (e.g., 3D tiles) from a remote source (e.g., cloud-based storage), and may use this pre-generated 3D data (e.g., a combination of 3D mesh, textures, and other geometry information) to augment local data (e.g., a point cloud of data collected by vehicle sensors) to determine much more information about a scene, including information about occluded or distant regions of the scene, than is available from the local data.

Adaptive vehicle augmented reality display using stereographic imagery

An apparatus, method, and computer readable medium related to determining the position of a device, for example determining the pose of a camera. Varying embodiments discuss the use of sensors and captured images to construct an environment property map, which provides reference information in the form of environment properties that are associated with positions, such as camera poses. Embodiments of the disclosure discuss using the environment property map online (e.g. in real time) in order to determine the position of a device. In some embodiments, the environment property map provides a coarse position that is used to refine or limit the necessary work for determining a more precise position.

System for determining position both indoor and outdoor

Various implementations disclosed are for detecting moving objects that are in a field of view of a head-mountable device (HMD). In various implementations, the HMD includes a display, an event camera, a non-transitory memory, and a processor coupled with the display, the event camera and the non-transitory memory. In some implementations, the method includes synthesizing a first optical flow characterizing one or more objects in a field of view of the event camera based on depth data associated with the one or more objects. In some implementations, the method includes determining a second optical flow characterizing the one or more objects in the field of view of the event camera based on event image data provided by the event camera. In some implementations, the method includes determining that a first object of the one or more objects is moving based on the first optical flow and the second optical flow.

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Los Gatos, CA, United States of America

Peter Georg Meier