Apple Inc.

Microsoft Technology Licensing, LLC

Alexandre Da Veiga

Bruno M Sommer

Vedant Saran

Spencer H Ray

Laura K Massey

Symeon Delikaris Manias

Tom G Salter

Tsao-Wei Huang

Bryan Cline

Michael I Weinstein

Alexander Menzies

Timothy R Pease

Amit Jain

Qi Shan

Jeffrey Sipko

Kenneth Liam Kiemele

Stephen Olivier Lemay

Jonathan R Dascola

Giancarlo Yerkes

Grant R Paul

Moinul H Khan

Hao Pan

Aaron Mackay Burns

Ben J Sugden

Jeffrey S Norris

Tobias Rick

Shai Messingher Lang

Bryce L Schmidtchen

Aditya Sankar

Roger Sebastian Sylvan

David H Huang

Shreyas V Joshi

Greg Alt

Jon Christen

Allison W Dryer

Lisa K Forssell

Peng Liu

Nicolas P Bonnier

Tobias Eble

Aaron Daniel Krauss

Benjamin Hunter Boesel

Christian Michael Sadak

Eric W Patterson

Michael Robert Thomas

Shih-Sang Chiu

Bradley Warren Peebler

Bryant Daniel Hawthorne

Ioana Negoita

Afshin Dehghan

Jonathan Perron

Robert K Molholm

Nikolai Michael Faaland

Roger Sebastian Kevin Sylvan

Benjamin John Sugden

Jonathan R Christen

Ye Cong

Andrew H Goulding

Ian Patterson Wood

George J Hito

Charles O Goddard

Embodiments disclosed herein are directed to devices, systems, and methods for separately processing an image stream to generate a first set of images for display by a set of displays and a second set of images for storage or transfer as part of a media capture event. Specifically, a first set of images from an image stream may be used to generate a first set of transformed images, and these transformed images may be displayed on a set of displays. A second set of images may be selected from the image stream in response to a capture request associated with a media capture event, and the second set of images may be used to generate a second set of transformed images. A set of output images may be generated from the second set of transformed images, and the set of output images may be stored or transmitted for later viewing.

Techniques for displaying and capturing images

Various implementations use object information to facilitate a communication session. Some implementations create a dense reconstruction (e.g., a point cloud or triangular mesh) of a physical environment, for example, using light intensity images and depth sensor data. A less data-intensive object information is also created to represent the physical environment for more efficient storage, editing, sharing, and use. In some implementations, the object information includes object attribute and location information. In some implementations, a 2D floorplan or other 2D representation provides object locations and metadata (e.g., object type, texture, heights, dimensions, etc.) provide object attributes. The object location and attribute information may be used, during a communication session, to generate a 3D graphical environment that is representative of the physical environment.

Communication sessions using object information

Various implementations disclosed herein include devices, systems, and methods that display visual content as part of a 3D environment and add audio corresponding to the visual content. The audio may be spatialized to be from one or more audio source locations within the 3D environment. For example, a video may be presented on a virtual surface within an extended reality (XR) environment while audio associated with the video is spatialized to sound as if it is produced from an audio source location corresponding to that virtual surface. How the audio is provided may be determined based on the position of the viewer (e.g., the user or his/her device) relative to the presented visual content.

Visual content presentation with viewer position-based audio

Various implementations disclosed herein include devices, systems, and methods that determines two-dimensional (2D) floorplans based on image data and refines the 2D floorplans using various user interface controls. For example, an example process may include obtaining a floorplan that is generated based on a sequence of images of a physical environment, identify a type of an object depicted in the floorplan, identify a context of the object depicted in the floorplan, and modify the floorplan based on the type and the context of the object.

2D floorplan pipeline and refinement

Various implementations disclosed herein include devices, systems, and methods that create a 3D video that includes determining first adjustments (e.g., first transforms) to video frames (e.g., one or more RGB images and depth images per frame) to align content in a coordinate system to remove the effects of capturing camera motion. Various implementations disclosed herein include devices, systems, and methods that playback a 3D video and includes determining second adjustments (e.g., second transforms) to remove the effects of movement of a viewing electronic device relative to a viewing environment during playback of the 3D video. Some implementations distinguish static content and moving content of the video frames to playback only moving objects or facilitate concurrent playback of multiple spatially related 3D videos. The 3D video may include images, audio, or 3D video of a video-capture-device user.

Spatial video capture and replay

In an exemplary process, a set of parameters corresponding to characteristics of a physical setting of a user is obtained. Based on the parameters, at least one display placement value and a fixed boundary location corresponding to the physical setting are obtained. In accordance with a determination that the at least one display placement value satisfies a display placement criterion, a virtual display is displayed at the fixed boundary location corresponding to the physical setting.

Displaying a virtual display

Various implementations provide a view of a 3D environment including a portal for viewing a stereo item (e.g., a photo or video) positioned a distance behind the portal. One or more visual effects are provided based on texture of one or more portions of the stereo item, e.g., texture at cutoff or visible edges of the stereo item. The effects change the appearance of the stereo item or the portal itself, e.g., improving visual comfort issues by minimizing window violations or otherwise enhancing the viewing experience. Various implementations provide a view of a 3D environment including an immersive view of a stereo item without using portal. Such a visual effect may be provided to partially obscure the surrounding 3D environment.

Visual techniques for 3D content

A method includes receiving one or more signals that each indicate a device type for a respective remote device, identifying one or more visible devices in one or more images, matching a first device from the one or more visible devices with a first signal from the one or more signals based on a device type of the first device matching a device type for the first signal and based on a visible output of the first device, pairing the first device with a second device, and controlling a function of the first device using the second device.

Discovery of and connection to remote devices

Various implementations disclosed herein include devices, systems, and methods that determine a 3D location of an edge based on image and depth data. This involves determining a 2D location of a line segment corresponding to an edge of an object based on a light-intensity image, determining a 3D location of a plane based on depth values (e.g., based on sampling depth near the edge/on both sides of the edge and fitting a plane to the sampled points), and determining a 3D location of the line segment based on the plane (e.g., by projecting the line segment onto the plane). The devices, systems, and methods may involve classifying an edge as a particular edge type (e.g., fold, cliff, plane) and detecting the edge based on such classification.

Edge locator

A method includes obtaining first pass-through image data characterized by a first pose. The method includes obtaining respective pixel characterization vectors for pixels in the first pass-through image data. The method includes identifying a feature of an object within the first pass-through image data in accordance with a determination that pixel characterization vectors for the feature satisfy a feature confidence threshold. The method includes displaying the first pass-through image data and an AR display marker that corresponds to the feature. The method includes obtaining second pass-through image data characterized by a second pose. The method includes transforming the AR display marker to a position associated with the second pose in order to track the feature. The method includes displaying the second pass-through image data and maintaining display of the AR display marker that corresponds to the feature of the object based on the transformation.

Methods and devices for detecting and identifying features in an AR/VR scene

In accordance with some embodiments, a technique that enables an electronic device with a camera to automatically gather and generate requisite data from the real-world environment to allow the electronic device to quickly and efficiently determine and provide accurate measurements of physical spaces and/or objects within the real-world environment is described.

Enabling automatic measurements

Various implementations disclosed herein include devices, systems, and methods that provide measurements of objects based on a location of a surface of the objects. An exemplary process may include obtaining a three-dimensional (3D) representation of a physical environment that was generated based on depth data and light intensity image data, generating a 3D bounding box corresponding to an object in the physical environment based on the 3D representation, determining a class of the object based on the 3D semantic data, determining a location of a surface of the object based on the class of the object, the location determined by identifying a plane within the 3D bounding box having semantics in the 3D semantic data satisfying surface criteria for the object, and providing a measurement of the object, the measurement of the object determined based on the location of the surface of the object.

Automatic measurements based on object classification

Various implementations disclosed herein include devices, systems, and methods that determines a distance between a portion (e.g., top) of a person's head and the floor below as an estimate of the person's height. For example, an example process may include determining a first position on a head of a person in a three-dimensional (3D) coordinate system, the first position determined based on detecting a feature on the head based on a two-dimensional (2D) image of the person in a physical environment, determining a second position on a floor below the first position in the 3D coordinate system, and estimating a height based on determining a distance between the first position and the second position.

Person height estimation

Systems and methods for localization for mobile devices are described. Some implementations may include accessing motion data captured using one or more motion sensors; determining, based on the motion data, a coarse localization, wherein the coarse localization includes a first estimate of position; obtaining one or more feature point maps, wherein the feature point maps are associated with a position of the coarse localization; accessing images captured using one or more image sensors; determining, based on the images, a fine localization pose by localizing into a feature point map of the one or more feature point maps, wherein the fine localization pose includes a second estimate of position and an estimate of orientation; generating, based on the fine localization pose, a virtual object image including a view of a virtual object; and displaying the virtual object image.

Localization for mobile devices

Growing community of inventors

San Francisco, CA, United States of America

Alexandre Da Veiga