Generating and storing digital media can be resource intensive processes. Some systems and methods disclosed herein relate to generating digital media using a pre-existing three-dimensional (3D) model of an object and feature points of the object. According to an embodiment, a method includes an e-commerce platform receiving a request for digital media depicting an object. In response to the request, the e-commerce platform may obtain a 3D model corresponding to the object and data pertaining to one or more feature points of the object. The one or more feature points may correspond to respective views of the 3D model. The e-commerce platform may then generate the digital media based on the 3D model and the one or more feature points, where the digital media could include renders of the 3D model depicting the one or more feature points.

In accordance with one or more embodiments herein, a system for creating a decision support material indicating damage to at least a part of an anatomical joint of a patient, wherein the created decision support material comprises one or more damage images, is provided. The system comprises a storage media and at least one processor, wherein the at least one processor is configured to i) receive a series of radiology images of the at least part of the anatomical joint from the storage media; ii) obtain a three-dimensional image representation of the at least part of the anatomical joint which is based on at least a part of said series of radiology images, by generating said three-dimensional image representation in an image segmentation process based on said series of radiology images, or receiving said three-dimensional image representation from a storage media; iii) identify tissue parts of the anatomical joint in at least one of at least a part of said series of radiology images and/or the three-dimensional image representation using image analysis; iv) determine damage to the identified tissue parts in the anatomical joint by analyzing at least one of at least a part of said series of radiology images and/or the three-dimensional image representation of the at least part of the anatomical joint; v) determine suitable sizes and suitable implanting positions for one or more graft plugs based on the determined damage; vi) mark damage to the anatomical joint and suitable sizes and implanting positions for the one or more graft plugs in the obtained three-dimensional image representation of the anatomical joint; and vii) generate a decision support material, where the determined damage to the at least part of the anatomical joint and the suitable sizes and implanting positions for the one or more graft plugs are marked in at least one of the one or more damage images of the decision support material, and at least one of the one or more damage images is generated based on the obtained three-dimensional image representation of the at least part of the anatomical joint.

Provided is a data processing method according to the present disclosure including: a scan data acquiring operation of acquiring scan data expressing an object, a reliability determining operation of determining a reliability of at least one evaluation area including at least one unit area for evaluating the scan data, and an indicating operation of indicating the evaluation area as a predetermined mark depending on the reliability of the evaluation area.

Embodiments include systems and methods for visualizing the position of a capturing device within a 3D mesh, generated from a video stream from the capturing device. A capturing device may provide a video stream along with point cloud data and camera pose data. This video stream, point cloud data, and camera pose data are then used to progressively generate a 3D mesh. The camera pose data and point cloud data can further be used, in conjunction with a SLAM algorithm, to indicate the position and orientation of the capturing device within the generated 3D mesh.

In some implementations, a computing device can present augmented reality (AR) labels in an AR video stream. For example, the computing device can obtain route information for a route requested by a user and can determine locations along the route for placing candidate AR labels. The computing device can determine the precise location of the computing device using camera depth information obtained in response to the user scanning the local real-world environment with a camera of the computing device. The computing device can select an AR label and/or label placement location for presentation in an AR video stream based on various criteria, including the distance between the candidate AR labels and the precise location of the computing device, priorities assigned to each candidate AR label, and/or whether a clear line of sight exists between the precise location of the computing device and the candidate AR label location.

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.

Disclosed is a method for using a virtual representation of an indoor environment to identify contents that have been damaged (e.g., by flooding). A virtual representation of a physical scene of an indoor environment is processed to identify a list of contents in the physical scene. The virtual representation may include 2-dimensional representations of the physical scene (e.g., images or video) or a 3-dimensional representation of the physical scene (e.g., 3D digital model). A reference line is determined in the virtual representation that is indicative of a maximum vertical extent of the damage in the physical scene. The position of the reference line is compared with the position of the identified contents in the virtual representation to determine contents that are likely to be damaged. For example, the contents that are at or below a plane represented by the reference line in the virtual representation may be identified as damaged.

System and methods are provided that generate a three-dimensional model from a physical space. While a user is scanning and/or recording the physical space with a user computing device, user speech describing the physical space is recorded. A transcript is generated from the audio captured during the scan and/or image recording of the physical space. Keywords from the transcript are used to improve computer-vision object identification, which is incorporated in the three-dimensional model.

Method and apparatus for operating home appliances, the method includes identifying a home appliance in a field view of a camera of an augmented reality (AR) device; generating, based on the home appliance that was identified, a control interface to overlay on a screen of the AR device or to present within an augmented view of a user that carries the AR device; detecting a selection by the user of control options on the control interface; and triggering the home appliance to perform an operation based on the selection.

Disclosed herein are systems and methods for presenting and annotating virtual content. According to an example method, a virtual object is presented to a first user at a first position via a transmissive display of a wearable device. A first input is received from the first user. In response to receiving the first input, a virtual annotation is presented at a first displacement from the first position. A first data is transmitted to a second user, the first data associated with the virtual annotation and the first displacement. A second input is received from the second user. In response to receiving the second input, the virtual annotation is presented to the first user at a second displacement from the first position. Second data is transmitted to a remote server, the second data associated with the virtual object, the virtual annotation, the second displacement, and the first position.