Apple Inc.

Other

Metaio Gmbh

Metalo Gmbh

Daniel Ulbricht

Angela Blechschmidt

Eshan Verma

Mohammad Haris Baig

Tanmay Batra

Chen-Yu Lee

Amit Kumar K C

Ian M Richter

Alexander S Polichroniadis

Omar Elafifi

Amit Kumar Kc

Peter Meier

Selim Benhimane

Jean-Daniel E Nahmias

Thomas Olszamowski

Robert William Mayor

Vinay R Majjigi

Isaac Thomas Miller

Adam S Howell

Oleg Naroditsky

Patrick James Coleman

Sean P Cier

Hyojoon Bae

Christian Lipski

Saurabh Godha

Oliver Ruepp

Lejing Wang

Mark E Drummond

Payal Jotwani

Bo Morgan

Peter Georg Meier

Sebastian Lieberknecht

Jack R Greasley

Lorenz Kern

Mingshan Wang

Deep Chakraborty

Mohammed Haris Baig

Tobias Hall

In one implementation, a method of generating a scene graph includes generating the scene graph using an end-to-end scene graph generator comprising an integrated neural network. For example, in various implementations, the method includes obtaining an image representing a plurality of objects. The method includes determining a relationship vector indicating spatial relationships between a particular object of the plurality of objects and others of the plurality of objects. The method includes determining, based on the relationship vector, an object type of the particular object.

End-to-end scene graph generation

Implementations disclosed herein provide systems and methods that determine relationships between objects based on an original semantic mesh of vertices and faces that represent the 3D geometry of a physical environment. Such an original semantic mesh may be generated and used to provide input to a machine learning model that estimates relationships between the objects in the physical environment. For example, the machine learning model may output a graph of nodes and edges indicating that a vase is on top of a table or that a particular instance of a vase, V1, is on top of a particular instance of a table, T1.

Object relationship estimation from a 3D semantic mesh

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

Various implementations disclosed herein include devices, systems, and methods that uses object relationships represented in the scene graph to adjust the position of objects. For example, an example process may include obtaining a three-dimensional (3D) representation of a physical environment that was generated based on sensor data obtained during a scanning process, detecting positions of a set of objects in the physical environment based on the 3D representation, generating a scene graph for the 3D representation of the physical environment based on the detected positions of the set of objects, wherein the scene graph represents the set of objects and relationships between the objects, and determining a refined 3D representation of the physical environment by refining the position of at least one object in the set of objects based on the scene graph and an alignment rule associated with a relationship in the scene graph.

Object correction using scene graphs

An exemplary process obtains sensor data for a physical environment, generates a local scene graph for the physical environment based on the sensor data, wherein the local scene graph represents a set of objects and relationships between the objects, matches the local scene graph with a principal scene graph of a set of principal scene graphs, and executes one or more scripted actions involving the objects based on a narrative associated with the matched principal scene graph. In some implementations, the set of principal scene graphs is generated by generating local scene graphs for a plurality of environments, and generating individual scene graphs each representative of local scene graphs.

Scripted interactive scenarios based on scene graph matching

Various implementations manage an electronic asset by creating a representation of an electronic asset and its variants. This may be accomplished by identifying variants of an electronic asset, identifying a portion of a feature space associated with the asset and variants, and providing a representation corresponding to that portion of feature space. A fixed function classifier may be used to determine the points in the feature space for the electronic asset and its variants. The set of points produced for an asset and its variants using such a fixed function classifier will be near one another in feature space. Moreover, the area around such points will also represent points for other similar variations of the asset and thus, the portion of the feature space around the points can be considered the area of ownership for the electronic asset, e.g., it defines a boundary of what the creator is asserting is his or her creation.

Electronic asset management

In one implementation, a method of achieving an objective using a scene graph includes identifying a set of transition couples of a plurality of transition couples between sets of spatial relationships of the scene graph, wherein each spatial relationship of a respective first set of spatial relationships indicated by each transition couple of the set of transition couples is included in an initial set of spatial relationships or an respective second set of spatial relationships indicated by a previous transition couple and wherein a particular spatial relationship is included in a respective second set of spatial relationships of a last transition couple of the set of transition couples.

Scene graph assisted navigation

Implementations disclosed herein provide systems and methods that match a current relationship model associated with a user's current environment to a prior relationship model for a prior environment to determine that the user is in the same environment. The current relationship model is compared with the prior relationship model based on matching characteristics of the current relationship model with characteristics of the prior relationship model.

Scene graph-based scene re-identification

In one implementation, a method of generating a plane hypothesis is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method includes obtaining a plurality of points of a point cloud based on the image of the scene. The method includes obtaining an object classification set based on the image of the scene. Each element of the object classification set includes a plurality of pixels respectively associated with a corresponding object in the scene. The method includes detecting a plane within the scene by identifying a subset of the plurality of points of the point cloud that correspond to a particular element of the object classification set.

Plane detection using semantic segmentation

Location mapping and navigation user interfaces may be generated and presented via mobile computing devices. A mobile device may detect its location and orientation using internal systems, and may capture image data using a device camera. The mobile device also may retrieve map information from a map server corresponding to the current location of the device. Using the image data captured at the device, the current location data, and the corresponding local map information, the mobile device may determine or update a current orientation reading for the device. Location errors and updated location data also may be determined for the device, and a map user interface may be generated and displayed on the mobile device using the updated device orientation and/or location data.

Mobile device navigation system

In one implementation, a method of including a person in a CGR experience or excluding the person from the CGR experience is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes, while presenting a CGR experience, capturing an image of scene; detecting, in the image of the scene, a person; and determining an identity of the person. The method includes determining, based on the identity of the person, whether to include the person in the CGR experience or exclude the person from the CGR experience. The method includes presenting the CGR experience based on the determination.

Identity-based inclusion/exclusion in a computer-generated reality experience

Various implementations disclosed herein include devices, systems, and methods that enable faster and more efficient real-time physical object recognition, information retrieval, and updating of a CGR environment. In some implementations, the CGR environment is provided at a first device based on a classification of the physical object, image or video data including the physical object is transmitted by the first device to a second device, and the CGR environment is updated by the first device based on a response associated with the physical object received from the second device.

Visual search refinement for computer generated rendering environments

In one implementation, consumption of media content (such as video, audio, or text) is supplemented with an immersive synthesized reality (SR) map based on the media content. In various implementations described herein, the SR map includes a plurality of SR environment representations which, when selected by a user, cause display of a corresponding SR environment.

Content event mapping

Growing community of inventors

Sunnyvale, CA, United States of America

Daniel Ulbricht