Abstract: In one implementation, a method includes: while causing presentation of video content having a current plot setting, receiving a user input indicating a request to explore the current plot setting; obtaining synthesized reality (SR) content associated with the current plot setting in response to receiving the user input; causing presentation of the SR content associated with the current plot setting; receiving one or more user interactions with the SR content; and adjusting the presentation of the SR content in response to receiving the one or more user interactions with the SR content.

Abstract: A method for representing virtual information in a view of a real environment is provided that includes: providing a system setup including at least one display device, wherein the system setup is adapted for blending in virtual information on the display device in at least part of the view, determining a position and orientation of a viewing point relative to at least one component of the real environment, providing a geometry model of the real environment, providing at least one item of virtual information and a position of the at least one item of virtual information, determining whether the position of the item of virtual information is inside a 2D or 3D geometrical shape, determining a criterion which is indicative of whether the built-in real object is at least partially visible or non-visible in the view of the real environment, and blending in the at least one item of virtual information on the display device in at least part of the view of the real environment.

Abstract: 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.

Abstract: In one implementation, a method includes: obtaining image data from an image sensor; recognizing a portion of an object within the image data; obtaining synthesized reality (SR) content associated with the portion of the object; and displaying the SR content in association with the portion of the object. In some implementations, the SR content is dependent on the orientation of an electronic device or the user relative to the object. In some implementations, the SR content is generated based on sensor data associated with the object.

Abstract: In one implementation, a method includes: obtaining image data from an image sensor; recognizing a portion of an object within the image data; obtaining synthesized reality (SR) content—such as mixed reality, augmented reality, augmented virtuality, or virtual reality content—associated with the portion of the object; and displaying the SR content in association with the portion of the object. In some implementations, the SR content is dependent on the orientation of an electronic device or the user relative to the object. In some implementations, the SR content is generated based on sensor data associated with the object.

Abstract: In one implementation, a method of generating environment states is performed by a device including one or more processors and non-transitory memory. The method includes displaying an environment including an asset associated with a neural network model and having a plurality of asset states. The method includes receiving a user input indicative of a training request. The method includes selecting, based on the user input, a training focus indicating one or more of the plurality of asset states. The method includes generating a set of training data including a plurality of training instances weighted according to the training focus. The method includes training the neural network model on the set of training data.

Abstract: 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.

Abstract: 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.

Abstract: A method and system of representing a virtual object in a view of a real environment is provided which includes providing image information of a first image of at least part of a human face captured by a camera, providing at least one human face specific characteristic, determining an image area of the face in the first image as a face region, determining at least one first light falling on the face according to the face region and the at least one human face specific characteristic, and blending in the virtual object on a display device in the view of the real environment according to at least one first light.

Abstract: The invention relates to a method of representing a virtual object in a view of a real environment which comprises the steps of providing image information of a first image of at least part of a human face captured by a first camera, providing at least one human face specific characteristic, determining at least part of an image area of the face in the first image as a face region of the first image, determining at least one first light falling on the face according to the face region of the first image and the at least one human face specific characteristic, and blending in the virtual object on a display device in the view of the real environment according to the at least one first light. The invention also relates to a system for representing a virtual object in a view of a real environment.

Abstract: A vehicle including one or more sensors, a light detection and ranging (lidar) sensor and a lidar prediction system. The one or more sensors include an optical sensor, a radar sensor, or both, configured to capture sensor data of a particular view. The lidar sensor is configured to capture lidar data of the particular view. The lidar prediction system includes a predictive model. The lidar prediction system is configured to generate a predicted lidar frame comprising applying the predictive model to the sensor data and send the predicted lidar frame to an external system.

Abstract: 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.

Abstract: The invention relates to a method for representing a virtual object in a real environment, having the following steps: generating a two-dimensional image of a real environment by means of a recording device, ascertaining a position of the recording device relative to at least one component of the real environment, segmenting at least one area of the real environment unmarked in reality in the two-dimensional image for identifying at least one segment of the real environment in distinction to a remaining part of the real environment while supplying corresponding segmentation data, and merging the virtual object with the two-dimensional image of the real environment with consideration of the segmentation data such that at least one part of the segment of the real environment is removed from the image of the real environment. The invention permits any collisions of virtual objects with real objects that occur upon merging with a real environment to be represented in a way largely close to reality.

Abstract: In an exemplary process for providing content in an augmented reality environment, image data correspond to a physical environment are obtained. Based on the image data, predefined entities of the plurality of predefined entities in the physical environment are identified using classifiers corresponding to predefined entities. Based on the one or more of the identified predefined entities, a geometric layout of the physical environment is determined. Based on the geometric layout, an area corresponding to a particular entity is determined. The particular entity corresponds to one or more identified predefined entities. Based on the area corresponding to the particular entity, the particular entity in the physical environment is identified using classifiers corresponding to the determined area. Based on the identified particular entity, a type of the physical environment is determined.

Abstract: A radial turbomachine has a first housing part and a second housing part, which jointly form and delimit a flow channel. The first housing part forms a motor chamber for accommodating a drive motor, and the second housing part forms a gas inlet. Furthermore, a radial impeller is provided, which can be driven about an axis of rotation by the drive motor in order to suction a gas from outside the turbomachine through the gas inlet into the flow channel and to convey said gas out of the flow channel through a gas outlet to the outside. The first housing part or the second housing part forms the gas outlet at a radial distance from the axis of rotation and peripherally delimits the gas outlet.

Abstract: In one implementation, a method of generating an environment state is performed by a device including one or more processors and non-transitory memory. The method includes obtaining a first environment state of an environment, wherein the first environment state indicates the inclusion in the environment of a first asset associated with a first timescale value and a second asset associated with a second timescale value, wherein the first environment state further indicates that the first asset has a first state of the first asset and the second asset has a first state of the second asset. The method includes determining a second state of the first asset and the second asset based on the first and second timescale value. The method includes determining a second environment state that indicates that the first asset has the second state and the second asset has the second state.

Abstract: In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.

Abstract: A method for representing virtual information in a view of a real environment is provided that includes: providing a system setup including at least one display device, wherein the system setup is adapted for blending in virtual information on the display device in at least part of the view, determining a position and orientation of a viewing point relative to at least one component of the real environment, providing a geometry model of the real environment, providing at least one item of virtual information and a position of the at least one item of virtual information, determining whether the position of the item of virtual information is inside a 2D or 3D geometrical shape, determining a criterion which is indicative of whether the built-in real object is at least partially visible or non-visible in the view of the real environment, and blending in the at least one item of virtual information on the display device in at least part of the view of the real environment.

Abstract: 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.