Snap Inc.

Weston Welge

Ramzi Zahreddine

Sagi Katz

Daniel Wagner

Patrick Timothy McSweeney Simons

Daniel Colascione

Matthias Kalkgruber

Tiago Miguel Pereira Torres

Kenneth Kubala

Jake Kaiser

An extended Reality (XR) system that provides services for determining three-dimensional (3D) data of physical objects in a real-world scene is provided. The XR system receives a request from an application to initiate a spatial scan of a real-world scene. In response, the XR system captures video frame data of the real-world scene and captures a pose of the XR system. The XR system determines a physical object in the real-world scene and determines a two-dimensional (2D) position of the physical object, using the video frame data. The XR system determines a depth of the physical object using the 2D position and determines a 3D position of the physical object in the real-world scene using the 2D position of the physical object, the depth of the physical object, and the pose of the XR system. The XR system communicates the 3D position data to the application.

Foveated sensing for extended reality

A system for hand tracking for an Augmented Reality (AR) system. The AR system uses a camera of the AR system to capture tracking video frame data of a hand of a user of the AR system. The AR system generates a skeletal model based on the tracking video frame data and determines a location of the hand of the user based on the skeletal model. The AR system causes a steerable camera of the AR system to focus on the hand of the user.

Steerable camera for AR hand tracking

A system for deformation or bending correction in an Augmented Reality (AR) system. Sensors are positioned in a frame of a head-worn AR system to sense forces or pressure acting on the frame by temple pieces attached to the frame. The sensed forces or pressure are used in conjunction with a model of the frame to determine a corrected model of the frame. The corrected model is used to correct video data captured by the AR system and to correct a video virtual overlay that is provided to a user wearing the head-worn AR system.

AR system bending correction

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vison, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

Varied depth determination using stereo vision and phase detection auto focus (PDAF)

An electronic eyewear device with a shape memory alloy (SMA) actuator to apply torque to the eyewear temples. The torque presses the eyewear temples against the side of a user's head for a snug and comfortable fit. The SMA actuator allows one size of eyewear frames to fit a larger range of users, thereby reducing the number of sizes required to be manufactured for the electronic eyewear device.

Eyewear with a shape memory alloy actuator

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using, stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vison, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

Disclosed are systems, methods, and non-transitory computer-readable media for varied depth determination using stereo vision and phase detection auto focus (PDAF). Computer stereo vision (stereo vision) is used to extract three-dimensional information from digital images. To utilize stereo vision, two optical sensors are displaced horizontally from one another and used to capture images depicting two differing views of a real-world environment from two different vantage points. The relative depth of the objects captured in the images is determined using triangulation by comparing the relative positions of the objects in the two images. For example, the relative positions of matching objects (e.g., features) identified in the captured images are used along with the known orientation of the optical sensors (e.g., distance between the optical sensors, vantage points the optical sensors) to estimate the depth of the objects.

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Boulder, CO, United States of America

Weston Welge