Qualcomm Incorporated

Abhijeet Bisain

Gerhard Reitmayr

Wesley James Holland

Brian D Vogelsang

Jonathan Kies

Ashwin Deepak Swaminathan

Michael Franco Taveira

William Whyte

Muralidhar Reddy Akula

Peerapol Tinnakornsrisuphap

Prashanth Haridas Hande

Chih-Ping Li

Ajit Deepak Gupte

Ajit Venkat Rao

Shankar Sadasivam

Aditya Narain Srivastava

Ramesh Chandrasekhar

Pushkar Gorur Sheshagiri

Vitor Rocha De Carvalho

Tadeusz Jarosinski

Rajeev Jain

Hussein Metwaly Saad

Ryan Michael Carey

Suhas H Sheshadri

Bhuwan Dhingra

Jinwon Lee

Chayan Sharma

Piero Zappi

Santiago Mazuelas

David Arellanes

Suhas Hariharapura Sheshadri

Examples are described for processing images to mask dynamic objects out of images to improve feature tracking between images. A device receives an image of an environment captured by an image sensor. The image depicts at least a static portion of the environment and a dynamic object in the environment. The device identifies a portion of the image that includes a depiction of the dynamic object. For example, the device can detect a bounding box around the dynamic object, or can detect which pixels in the image correspond to the dynamic object. The device generates a masked image at least by masking the portion of the image. The device identifies features in the masked image, and uses the features from the masked image for feature tracking from other images of the environment, masked or otherwise. The device can use this feature tracking for mapping, localization, and/or relocation.

Object segmentation and feature tracking

Systems, methods, and non-transitory media are provided for localizing and mapping smart devices. An example method can include receiving, by an extended reality (XR) device, an identification output from a connected device that is coupled directly or indirectly to the XR device, the identification output including an audio pattern, a display pattern, and/or a light pattern; detecting the identification output from the connected device; and based on the identification output from the connected device, mapping the connected device in a coordinate system of the XR device.

Mapping networked devices

Systems and techniques are described for token device transfer management. A system identifies, in a payload of at least one block of a distributed ledger, a token corresponding to media content. A parameter of the token in the distributed ledger indicates that the token is associated with a first user. The system identifies a device that is associated with the token and the media content. The device is also associated with the first user. The system identifies that the device has been relocated to an area associated with a second user. In response to identifying that the device has been relocated to the area, the system causes the parameter of the token in the distributed ledger to be modified from indicating that the token is associated with the first user to indicating that the token is associated with the second user.

Systems and methods for management of non-fungible tokens and corresponding digital assets

Systems and techniques are described for situational token-associated media output. A system receives sensor data captured by at least one sensor of a media device. The system identifies, based on the sensor data, a relationship between the media device and an anchor element that is associated with a token. The system identifies the token in a payload of at least one block of a distributed ledger. The token corresponds to media content according to the distributed ledger. The system generates a representation of the media content corresponding to the token. In response to identifying the relationship between the media device and the anchor element, the system outputs the representation of the media content.

Systems and techniques are described herein for processing images. The systems and techniques can be implemented by various types of systems, such as by an extended reality (XR) system or device. In some cases, a first processor receives an image of an environment captured by an image sensor, identifies features depicted in the image, and generates descriptors for the features. The first processor sends the descriptors to a second processor, which may be more powerful than the first processor. The second processor receives the descriptors. The second processor associates the plurality of features with a map of the environment based on at least a subset of the plurality of descriptors. For example, the second processor can track at least a subset of the features based on at least a subset of the descriptors and based on feature information from one or more additional images of the environment.

Feature processing in extended reality systems

This disclosure provides systems, methods, and devices for wireless communication that support enhanced beam management using extended reality (XR) perception data. In a first aspect, a method of wireless communication includes establishing a communication connection between a user equipment (UE) and a serving base station using a current serving beam selected by the UE from a plurality of available beams paired with a serving base station beam. The method further includes obtaining, perception information from one or more extended reality sensors associated with the UE and determining, in response to detection of UE movement, a transpositional representation of the movement using the perception information. The UE may then select a new serving beam in accordance with the transpositional representation. Other aspects and features are also claimed and described.

Exploiting extended reality perception data for beam management

Methods, systems, and devices for head wearable display devices are described. A device may capture a set of images over a set of orientations using a set of cameras positioned on an outward facing surface of the device. The set of images include a first subset of images captured by a first camera and a second subset of images captured by a second camera. The device may detect a set of facial features in each of the first subset of images and the second subset of images, and measure a set of inter-pupillary distances over the set of orientations based on the set of facial features in each of the first and second subset of images. The device may determine an inter-pupillary distance parameter based on aggregating the set of inter-pupillary distances over the set of orientations. The device may calibrate based on the inter-pupillary distance parameter.

Head wearable display device calibrated for distortion correction using inter-pupillary distance

A method of auto-calibrating light sensor data of a mobile device includes, obtaining, by the mobile device, one or more reference parameters representative of light sensor data collected by a reference device. The method also includes collecting, by the mobile device, light sensor data from a light sensor included in the mobile device, itself. One or more sample parameters of the light sensor data obtained from the light sensor included in the mobile device are then calculated. A calibration model is then determined for auto-calibrating the light sensor data of the light sensor included in the mobile device based on the one or more reference parameters and the one or more sample parameters.

Auto-calibrating light sensor data of a mobile device

A context aware system, for use in a mobile device, includes a context change detector (CCD) coupled to a context classifier (CCL). The CCD is configured to receive sensor data and to detect a change in a current context state of the mobile device based on the received sensor data. The CCL is configured to transition from a low power consumption mode to a normal power consumption mode in response to the CCD detecting the change in the current context state. The CCL is further configured to determine a next context state of the mobile device while in the normal power consumption mode.

Context aware system with multiple power consumption modes

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San Diego, CA, United States of America

Abhijeet Bisain