Abstract: Embodiments include systems and methods for offloading media service operation to one or graphical processing unit (GPUs). In some embodiments, a computer system, includes a first computer device to provide a media service that involves implementing media service operations and transmit a first media service request for a first media service operation of the media service operations. In addition, the computer system includes a second computer device that includes one or more GPUs. The second computer device is to implement the first media service operation with the one or more GPUs in response to the first computer device transmitting the first media service request for the first media service operation.

Abstract: An energy absorbing headrest for a vehicle includes a base member, a frame pivotally mounted to the base member, and a biasing member mounted between the base member and the frame. The biasing member provides a biasing force to the frame. A release mechanism is mounted to the base member and retains the frame against the biasing force. The release mechanism selectively releases the frame upon receipt of a release signal.

Abstract: Embodiments include systems and methods for filtering augmented reality (“AR”) data streams, and in particular, video and/or audio streams to change or remove undesirable content, such as personally identifiable information, embarrassing content, financial data, etc. For example, a handheld device may capture video using a built in camera, and as needed superimpose AR objects on the video to change its content. A cloud service may host an AR session between the handheld device and a remote machine. The cloud service and remote machine may also operate their own filter on the AR data to remove and/or replace content to comply with their interests, regulations, policies, etc. Filtering may be cumulative. For example, the handheld device may remove financial data before it leaves the device, and then the cloud service may replace commercial logos before sharing AR data between session participants.

Abstract: Methods and systems disclosed herein are directed to detection and recognition of items of data on labels applied to equipment and identifying metadata labels for the items of data using NLP. Embodiments may include identifying one or more items of data on an image of a label associated with a piece of equipment, determining, using NLP on the one or more items of data of the image, one or more metadata associated, respectively, with the identified one or more items of data, and outputting at least one of the one or more metadata and associated items of data.

Abstract: Embodiments include systems and methods for creation of a 3D mesh from a video stream or a sequence of frames. A sparse point cloud is first created from the video stream, which is then densified per frame by comparison with spatially proximate frames. A 3D mesh is then created from the densified depth maps, and the mesh is textured by projecting the images from the video stream or sequence of frames onto the mesh. Metric scale of the depth maps may be estimated where direct measurements are not able to be measured or calculated using a machine learning depth estimation network.

Abstract: Embodiments include systems and methods for remotely measuring distances in an environment captured by a device. A device captures a video stream of a device along with AR data that may include camera pose information and/or depth information, and transmits the video stream and AR data to a remote device. The environment may be analyzed to identify objects such as lines, edges, curves, shapes, anchors/corners, products, e.g., appliances, and other things of interest. Some or all of objects may be identified to the remote device to facilitate selecting an object or region of interest. Selected points for an object may be more precisely located by snapping them to corresponding anchor points for an object. Using anchor points facilitates more precise identification and/or measurement of an aspect of an object, such as one of its dimensions, the volume of a space, or performing other actions such as replacement of an object.

Abstract: Embodiments include systems and methods for creation of a 3D mesh from a video stream or a sequence of frames. A sparse point cloud is first created from the video stream, which is then densified per frame by comparison with spatially proximate frames. A 3D mesh is then created from the densified depth maps, and the mesh is textured by projecting the images from the video stream or sequence of frames onto the mesh. Metric scale of the depth maps may be estimated where direct measurements are not able to be measured or calculated using a machine learning depth estimation network.

Abstract: Embodiments include systems and methods for determining a 6D pose estimate associated with an image of a physical 3D object captured in a video stream. An initial 6D pose estimate is inferred and then further iteratively refined. The video stream may be frozen to allow the user to tap or touch a display to indicate a location of the user-input keypoints. The resulting 6D pose estimate is used to assist in replacing or superimposing the physical 3D object with digital or virtual content in an augmented reality (AR) frame.

Abstract: Embodiments include systems and methods for generating a 3D mesh on a server from one or more images and other data of a physical space that is captured by a device. The device may capture images with RGB data that includes depth information for pixels of the one or more images. The server may then create or update a 3D mesh based upon the images and depth information. The server may also use received images to provide textures to the various surfaces of the 3D mesh. The server may then cause a 3D model of the physical space to be viewed by another device, either by transmitting the 3D mesh for the other device used to construct a 3D model, or by rendering the 3D model directly on the other device. Other embodiments may be described and/or claimed.

Abstract: A platform for synchronizing augmented reality (AR) views and information between two or more network connected devices is disclosed. A first device captures a video stream and associated essential meta-data, embeds the essential meta-data into the video stream and transmits it to a second device. The second device receives the video stream, extracts the essential meta-data, inserts one or more AR objects into the video stream with reference to the enhanced meta-data, and transmits to the first device the AR objects and reference to the essential meta-data. The first device renders the one or more AR objects into the video stream, using the essential meta-data references to locate the AR objects in each video stream frame. The second device may also determine and transmit a modified video stream to the first device.

Abstract: Embodiments include systems and methods for determining a 6D pose estimate associated with an image of a physical 3D object captured in a video stream. An initial 6D pose estimate is inferred and then further iteratively refined. The video stream may be frozen to allow the user to tap or touch a display to indicate a location of the user-input keypoints. The resulting 6D pose estimate is used to assist in replacing or superimposing the physical 3D object with digital or virtual content in an augmented reality (AR) frame.

Abstract: Methods and systems disclosed herein are directed to detection and recognition of items of data on labels applied to equipment and identifying metadata labels for the items of data using NLP. Embodiments may include identifying one or more items of data on an image of a label associated with a piece of equipment, determining, using NLP on the one or more items of data of the image, one or more metadata associated, respectively, with the identified one or more items of data, and outputting at least one of the one or more metadata and associated items of data.