Abstract: An apparatus configured for head-worn by a user, includes: a screen configured to present graphics for the user; a camera system configured to view an environment in which the user is located; and a processing unit coupled to the camera system, the processing unit configured to: obtain a first image with a first resolution, the first image having a first corner, determine a second image with a second resolution, the second image having a second corner that corresponds with the first corner in the first image, wherein the second image is based on the first image, the second resolution being less than the first resolution, detect the second corner in the second image, determine a position of the second corner in the second image, and determine a position of the first corner in the first image based at least in part on the determined position of the second corner in the second image.

Abstract: An image processing apparatus includes a display section that displays a background image and a processing target image on a display screen, a receiving section that receives an input position on the display screen input by user, and a processing section that processes editing to the processing target image based on a relationship between the input position and a position of the processing target image.

Abstract: A computer-implemented method for processing a set of virtual fibers into a set of clusters of virtual fibers, usable for manipulation on a cluster basis in a computer graphics generation system, may include determining aspects for virtual fibers in the set of virtual fibers, determining similarity scores between the virtual fibers based on their aspects, and determining an initial cluster comprising the virtual fibers of the set of virtual fibers. The method may further include instantiating a cluster list in at least one memory, adding the initial cluster to the cluster list, partitioning the initial cluster into a first subsequent cluster and a second subsequent cluster based on similarity scores among fibers in the initial cluster, adding the first subsequent cluster and the second subsequent cluster to the cluster list, and testing whether a number of clusters in the cluster list is below a predetermined threshold.

Abstract: An electronic apparatus performs a method of constructing a facial position map from a two-dimensional (2D) facial image of a real-life person that includes: generating a coarse facial position map from the 2D facial image; predicting a first set of keypoints in the 2D facial image based on the coarse facial position map; identifying a second set of keypoints in the 2D facial image based on the user-provided keypoint annotations; and updating the coarse facial position map to get a final facial position map so as to reduce the differences between the first set of keypoints and the second set of key points in the 2D facial image. In some embodiments, a final set of keypoints and/or a three-dimensional (3D) facial model of the real-life person is formed from the final facial position map.

Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: An electronic apparatus performs a method of generating a three-dimensional (3D) head deformation model that includes: receiving a two-dimensional (2D) facial image; identifying a first set of keypoints in the 2D facial image based on some artificial intelligence (AI) models; mapping the first set of keypoints to a second set of keypoints based on a set of user-provided keypoint annotations located on a plurality of vertices of a mesh of a 3D head template model; performing deformation to the mesh of the 3D head template model to obtain a deformed 3D head mesh model by reducing the differences between the first set of keypoints and the second set of keypoints; and applying a blendshape method to the deformed 3D head mesh model to obtain a personalized head model according to the 2D facial image.

Abstract: A virtual reality apparatus and method are described for tile-based rendering. For example, one embodiment of an apparatus comprises: a set of on-chip geometry buffers including a first buffer to store geometry data, and a set of pointer buffers to store pointers to the geometry data; a tile-based immediate mode rendering (TBIMR) module to perform tile-based immediate mode rendering using geometry data and pointers stored within the set of on-chip geometry buffers; spill circuitry to determine when the on-chip geometry buffers are over-subscribed and responsively spill additional geometry data and/or pointers to an off-chip memory; and a prefetcher to start prefetching the geometry data from the off-chip memory as space becomes available within the on-chip geometry buffers, the TBIMR module to perform tile-based immediate mode rendering using the geometry data prefetched from the off-chip memory.

Abstract: The present invention relates to a method of determining the coherence between a physical object and a numerical model representative of the shape of a physical object, wherein a specific use of the method according to the invention is assessing and quantifying manufacturing defects. The method is characterized by a set of steps comprising: capturing multiple images of the physical object; processing the images to produce a second numerical model of the physical object; in a computer, aligning the first numerical model and the second numerical model to generate a third numerical model according to specific sub-steps, wherein the third numerical model comprises a plurality of points representative of the shape of the captured physical object. Compared with the first numerical model, the third numerical model allows determining a measurement of the coherence between the physical object captured by means of images and the first numerical model representative thereof.

Abstract: The invention relates to a medical instrument guiding device comprising the medical instrument, a monitoring device (2) comprising a support (3) and a medical imaging probe (4) which is arranged on the support, a screen (10), and a control unit (11) of the device which is connected to the screen and the probe for generating at least one three-dimensional image, the control unit being configured to generate at least one two-dimensional image on the screen showing a deformation of the instrument from at least the three-dimensional image, the control unit being configured to estimate a virtual path of the instrument from the deformation of the instrument for extending the insertion thereof to a target, and deduce therefrom at least one distance between the virtual path and the target.

Abstract: The present disclosure is directed to a software tool that facilitates coordination between various parties that are involved in the process of rectifying a problem identified in a combined three-dimensional model file. In one implementation, the software tool may cause a computing device to (a) receive an indication requesting creation of a coordination issue that relates to a portion of a rendered three-dimensional view of a construction project, (b) in response to the receipt of the indication, create a data set defining the coordination issue, the data set including (i) a representation of the portion of the rendered three-dimensional view, and (ii) data indicating an assignee of the coordination issue, and (c) cause an indication of the coordination issue to be presented to a client station associated with the assignee.

Abstract: A method of generating hologram data in a holographic display apparatus, including a hologram processing device generating hologram data and a display terminal reconstructing a hologram image in a three-dimensional (3D) space on the basis of the generated hologram data, includes generating depth map image data from input image data by using a deep learning engine for generating a depth map and calculating a complex value hologram on the basis of the depth map image data and the input image data to generate the hologram data.

Abstract: In example embodiments, techniques are provided to automatically classify individual elements of an infrastructure model by training one or more machine learning algorithms on classified infrastructure models, producing a classification model that maps features to classification labels, and utilizing the classification model to classify the individual elements of the infrastructure model. The resulting classified elements may then be readily subject to analytics, for example, enabling the display of dashboards for monitoring project performance and the impact of design changes. Such techniques enable classification of elements of new infrastructure models or in updates to existing infrastructure models.

Abstract: Provided are an imaging evaluation map, an imaging evaluation map generating device, an imaging evaluation map generating method, and an imaging evaluation map generating program capable of easily making an imaging plan.

Abstract: A virtual world generation engine and methods for generating virtual worlds from images collected from various sources, including crowdsourced images. A virtual world generation engine may obtain images (e.g., digital photographs, digital video frames, etc.) related to a particular real-world scene, combine the images using image processing techniques such as image stitching techniques to generate composite images representing a view of the scene, and generate models from the composite images. The models may be used in rendering video content representing virtual worlds generated from the collected images of real-world scenes; the video content may be streamed to client device(s). Obtaining the images, generating models, rendering video content from the models, and streaming the video content may be performed in response to user interactions with video content on the client device(s) to allow interactive exploration of the virtual worlds in real-time or near-real time.

Abstract: Disclosed are editing tools for manipulating a three-dimensional (“3D”) data file or point cloud. An editing application may generate a visualization of the 3D data file or point cloud, and a user may invoke an editing tool over a particular region of the visualization that is rendered based on the positional and non-positional values of a first data point set and a second data point set from the 3D data file or point cloud. The editing tool may differentiate the first data point set from the second data point set based on unique commonality in the positional and/or non-positional values of the first data point set, and may edit less than all of the particular region by adjusting one or more of the positional and/or non-positional values of the first data point set while retaining the positional and non-positional values of the second data point set.

Abstract: The subject technology receives, at a client device, a selection of a selectable graphical item from a plurality of selectable graphical items, the selectable graphical item comprising an augmented reality content generator including a 3D effect. The subject technology captures image data using at least one camera of the client device. The subject technology generates depth data using a machine learning model based at least in part on the captured image data. The subject technology applies, to the image data and the depth data, the 3D effect based at least in part on the augmented reality content generator.

Abstract: In some embodiments, a method comprises receiving a plurality of digital images depicting an indoor scene of a room and captured using a capture device, and trajectory data of a trajectory that the capture device followed as the capture device was capturing the images; wherein the images and the trajectory data satisfy quality checks, including a closed loop check that requires that first images, of the plurality of digital images, and last images, of the plurality of digital images, overlap; determining a point cloud by preprocessing the digital images and the trajectory data to satisfy optimization constraints, and by identifying and labelling a plurality of feature points in data obtained by preprocessing the digital images and the trajectory data; based on the point cloud and the trajectory data, generating room geometry for the room and a 3D model of the room; and displaying a graphical representation of the 3D model.

Abstract: The present disclosure generally relates to user interfaces for adjusting simulated image effects. In some embodiments, user interfaces for adjusting a simulated depth effect is described. In some embodiments, user interfaces for displaying adjustments to a simulated depth effect is described. In some embodiments, user interfaces for indicating an interference to adjusting simulated image effects is described.

Abstract: This disclosure describes implementations of a three-dimensional (3D) scene recovery system that reconstructs a 3D scene representation of a scene portrayed in a single digital image. For instance, the 3D scene recovery system trains and utilizes a 3D point cloud model to recover accurate intrinsic camera parameters from a depth map of the digital image. Additionally, the 3D point cloud model may include multiple neural networks that target specific intrinsic camera parameters. For example, the 3D point cloud model may include a depth 3D point cloud neural network that recovers the depth shift as well as include a focal length 3D point cloud neural network that recovers the camera focal length. Further, the 3D scene recovery system may utilize the recovered intrinsic camera parameters to transform the single digital image into an accurate and realistic 3D scene representation, such as a 3D point cloud.

Abstract: An apparatus configured for head-worn by a user, includes: a screen configured to present graphics for the user; a camera system configured to view an environment in which the user is located; and a processing unit coupled to the camera system, the processing unit configured to: obtain a first image with a first resolution, the first image having a first corner, determine a second image with a second resolution, the second image having a second corner that corresponds with the first corner in the first image, wherein the second image is based on the first image, the second resolution being less than the first resolution, detect the second corner in the second image, determine a position of the second corner in the second image, and determine a position of the first corner in the first image based at least in part on the determined position of the second corner in the second image.