Abstract: One or more disclosed techniques comprise generating an extended reality (XR) template at a first location, where the XR template represents a virtual space in an XR environment that is associated with a real-world object at a second location, and generating a template object that represents the real-world object, wherein the template object is included in the XR template. The techniques further comprise modifying the template object to include a marker that corresponds to a real-world marker associated with the real-world object, generating a first template XR object that corresponds a first XR object to be displayed in the XR environment, where the first template XR object is positioned within the XR template relative to the marker included on the template object, and assigning the XR template to the real-world marker.

Abstract: An information processing device for drawing an object arranged in a three-dimensional virtual space, in an expression viewed from a virtual camera, the object including a field object associated with a two-dimensional plane defined by a first axis and a second axis, and a specific object arranged on the field object, the device including: a change processing unit changing a region of the field object falling within a viewing angle of the virtual camera; and a deformation processing unit deforming the field object, in which in a case where the region is changed by the change processing unit, the deformation processing unit makes a deformation mode of the field object different when it is determined that the specific object is positioned in the region after being changed and when it is determined that the specific object is not positioned in the region after being changed.

Abstract: In one embodiment, a method includes generating a front panel of a garment based on one or more images including the garment, generating a back panel of the garment, aligning the front panel and the back panel in a three-dimensional space so that the front panel is in front of a three-dimensional body and the back panel is behind the three-dimensional body, identifying one or more pairs of boundary segments of the front panel and the back panel, wherein each pair of boundary segments of the front panel and the back panel are to be attached together, and generating a digital garment by attaching each of the identified one or more pairs of boundary segments of the front panel and the back panel through a plurality of iterative simulations using a physics simulation model.

Abstract: A method of constructing a bounding box comprises: acquiring a set of sensed data points; adding, for each sensed data point, at least one calculated data point; and defining a bounding box containing the sensed and calculated data points. A method of identifying voxels in a voxel grid corresponding to a plurality of data points comprises: calculating, for each data point, a distance between it and each voxel; creating a subset of voxels comprising voxels having a distance from one data point that is less than a predetermined distance; creating another subset comprising those voxels that neighbor a voxel in the first subset; computing, for each voxel in the second subset, a distance between it and each voxel in the first subset; and identifying each voxel in the first subset that is a distance away from each voxel in the second subset that exceeds a predetermined distance.

Abstract: An Artificial Intelligence (AI) three-dimensional modeling system that analyzes and segments imagery of a room, generates a three-dimensional model of the room from the segmented imagery, identifies objects within the room, and conducts an assessment of the room based on the identified objects.

Abstract: A system for generating slice data for additive manufacturing, comprises a graphics processing unit (GPU) that receives a digital model of an object in a three-dimensional build space defined over a plurality of slices, computes a three-dimensional signed distance field over voxels in the build space, assigns a building material to each voxel based on a respective distance field value, and generates slice data output pertaining to the building material assignments for each slice. The slice data output can be used for printing the object in layers corresponding to the slices. The distance field comprises one or more vector having a vertical component with respect to the slices.

Abstract: Apparatus and method for correcting image regions following upsampling or frame interpolation. For example, one embodiment of an apparatus comprises a machine-learning engine to evaluate at least a first image in a sequence of images generated by a real-time interactive application, the machine learning engine to responsively use previously learned data to generate an upsampled or interpolated image comprising a plurality of pixel patches. In one embodiment, each pixel patch is associated with a confidence value reflecting how accurately the pixel patch was generated by the machine learning engine. A selective ray tracing engine identifies a first pixel patch to be corrected based a first confidence value corresponding to the first pixel patch being lower than a threshold and performs ray tracing operations on a first portion of the first image to generate a corrected first pixel patch.

Abstract: Examples are disclosed that relate to displaying a hologram via an HMD. One disclosed example provides a method comprising obtaining depth data from a direct-measurement depth sensor included in the case for the HMD, the depth data comprising a depth map of a real-world environment. The method further comprises determining a distance from the HMD to an object in the real-world environment using the depth map, obtaining holographic imagery for display based at least upon the distance, and outputting the holographic imagery for display on the HMD.

Abstract: Methods and tiling engines for tiling primitives in a tile based graphics processing system in which a rendering space is divided into a plurality of tiles. The method includes generating a multi-level hierarchy of tile groups, each level of the multi-level hierarchy comprising one or more tile groups comprising one or more of the plurality of tiles; receiving a plurality of primitive blocks, each primitive block comprising geometry data for one or more primitives; associating each of the plurality of primitive blocks with one or more of the tile groups up to a maximum number of tile groups such that if at least one primitive of a primitive block falls, at least partially, within the bounds of a tile, the primitive block is associated with at least one tile group that includes that tile; and generating a control stream for each tile group based on the associations, wherein each control stream comprises a primitive block entry for each primitive block associated with the corresponding tile group.

Abstract: A graphics processing architecture in one example performs vertex manipulation operations and pixel manipulation operations by transmitting vertex data to a general purpose register block, and performing vertex operations on the vertex data by a processor unless the general purpose register block does not have enough available space therein to store incoming vertex data; and continues pixel calculation operations that are to be or are currently being performed by the processor based on instructions maintained in an instruction store until enough registers within the general purpose register block become available.

Abstract: The disclosure relates to a system and a method for generating a neural network model for image processing by interacting with at least one client terminal. The method may include receiving via a network, a plurality of first training samples from the at least one client terminal. The method may also include training a first neural network model based on the plurality of first training samples to generate a second neural network model. The method may further include transmitting, via the network, the second neural network model to the at least one client terminal.

Abstract: The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

Abstract: A system configured to assist a user in scanning a physical environment in order to generate a three-dimensional scan or model. In some cases, the system may include an interface to assist the user in capturing data usable to determine a scale or depth of the physical environment and to perform a scan in a manner that minimizes gaps.

Abstract: Three-dimensional (3D) maps may be generated for different areas based on scans of the areas using sensor(s) of a mobile computing device. During each scan, locations of the mobile computing device can be measured relative to a fixed-positioned smart device using ultra-wideband communication (UWB). The 3D maps for the areas may be registered to the fixed position (i.e., anchor position) of the smart device based on the location measurements acquired during the scan so that the 3D maps can be merged into a combined 3D map. The combined (i.e., merged) 3D map may then be used to facilitate location-specific operation of the mobile computing device or other smart device.

Abstract: In one implementation, a virtual-world simulator includes a computing platform having a hardware processor and a memory storing a software code, a tracking system communicatively coupled to the computing platform, and a projection device communicatively coupled to the computing platform. The hardware processor is configured to execute the software code to obtain a map of a geometry of a real-world venue including the virtual-world simulator, to identify one or more virtual effects for display in the real-world venue, and to use the tracking system to track a moving perspective of one of a user in the real-world venue or a camera in the real-world venue. The hardware processor is further configured to execute the software code to control the projection device to simulate a virtual-world by conforming the identified one or more virtual effects to the geometry of the real-world venue from a present vantage point of the tracked moving perspective.

Abstract: Various implementations disclosed herein include devices, systems, and methods that generates a three-dimensional (3D) model based on a selected subset of the images and depth data corresponding to each of the images of the subset. For example, an example process may include acquiring sensor data during movement of the device in a physical environment including an object, the sensor data including images of a physical environment captured via a camera on the device, selecting a subset of the images based on assessing the images with respect to motion-based defects based on device motion and depth data, and generating a 3D model of the object based on the selected subset of the images and depth data corresponding to each of the images of the selected subset.

Abstract: Systems, devices, methods, and computer-readable media for horizon-based navigation. A method can include receiving image data corresponding to a geographical region in a field of view of an imaging unit and in which the device is situated, based on the received image data, generating, by the processing unit, an image horizon corresponding to a horizon of the geographical region and from a perspective of the imaging unit, projecting three-dimensional (3D) points of a 3D point set of the geographical region to an image space of the received image data resulting in a synthetic image, generating, by the processing unit, a synthetic image horizon of the synthetic image, and responsive to determining the image horizon sufficiently correlates with the synthetic image horizon, providing a location corresponding to a perspective of the synthetic image as a location of the processing unit.

Abstract: A spirometry system includes an imaging device configured to capture upper body movement images of a subject during inhalation and exhalation of the subject. The system further includes at least one controller configured to receive the captured images from the imaging device and, based upon the received images, determine at least one of an image-based spirometry flow-volume curve for the subject or an image-based spirometry parameter for the subject.

Abstract: A system for dense depth computation aided by sparse feature matching generates a first image using a first camera, a second image using a second camera, and a third image using a third camera. The system generates a sparse disparity map using the first image and the third image by (1) identifying a set of feature points within the first image and a set of corresponding feature points within the third image, and (2) identifying feature disparity values based on the set of feature points and the set of corresponding feature points. The system also applies the first image, the second image, and the sparse disparity map as inputs for generating a dense disparity map.

Abstract: Methods and systems for constructing a three-dimensional (3D) model of a user in a virtual environment for full body virtual reality (VR) applications are described. The method includes receiving an image of the user captured using an RGB camera; detecting a body bounding box associated with the user using a first trained neural network; determining a segmentation map of the user, based on the body bounding box; determining a two-dimensional (2D) contour of the user from the segmentation map; forming a 3D extrusion model by extruding the 2D contour; and constructing the 3D model of the user in the virtual environment by applying a geometric transformation to the 3D extrusion model. Applications of full body VR include physical training and fitness sessions, games, control of computing devices, manipulation and display of data, interactive social media with VR, and the like.