Altering properties of rendered objects and/or mixed reality environments utilizing control points associated with the rendered objects and/or mixed reality environments is described. Techniques described can include detecting a gesture performed by or in association with a control object. Based at least in part on detecting the gesture, techniques described can identify a target control point that is associated with a rendered object and/or a mixed reality environment. As the control object moves within the mixed reality environment, the target control point can track the movement of the control object. Based at least in part on the movement of the control object, a property of the rendered object and/or the mixed reality environment can be altered. A rendering of the rendered object and/or the mixed reality environment can be modified to reflect any alterations to the property.

The present invention examines location and orientation of a mobile device (e.g., smartphone or wearable device) to select a video feed from a plurality of available video sources in communication with the mobile device via a network. The respective locations and orientations of the video sources are stored. A program, executing on the mobile device, uses GPS and accelerometers to establish the mobile device's location and orientation. Using the stored information about the video sources' locations and orientations and the mobile device's determined location and orientation, the program decides which video feed is appropriate and requests that feed from the respective video source. As the location and orientation of the mobile device changes, the program continues to compare the mobile device's location and orientation with the location and orientation information of the available video sources and requests new video feeds as needed in real-time.

To apply an edge effect to a 3D virtual object, a display system receives user input indicative of a desired display region of a 3D virtual object, defines a bounding volume corresponding to the desired display region, and clips the edges of the 3D virtual object to the surfaces of the bounding volume. The display system applies a visual edge effect to one or more of the clipped edges of the 3D virtual object, and displays to the user of the 3D virtual object with the visual edge effect. The technique can include selectively discarding pixels along a surface of the bounding volume, based on a depth map indicative of height values of the 3D virtual object at different horizontal pixel coordinates where the visual edge effect is applied only for edge pixels not discarded.

A method includes a computing device of a computing infrastructure identifying a non-fungible token (NFT) associated with a set of learning objects and establishing, with a user computing device, agreed licensing terms utilizing licensee information and based on available licensing terms of a smart contract for the set of learning objects. The method further includes generating a license smart contract for the set of learning objects to include the licensee information and the agreed licensing terms and causing generation of a license block affiliated with the NFT via a blockchain of the object distributed ledger.

A method includes a computing device generating a virtual reality environment utilizing a group of object representations in accordance with interaction information for at least some of the object representations of the group of object representations. The method further includes the computing device outputting the virtual reality environment to a requesting entity for interactive consumption. The method further includes the computing device generating a consumption block to indicate performance aspects of the interactive consumption by the requesting entity. The method further includes the computing device causing affiliation of the consumption block with a non-fungible token (NFT) associated with the requesting entity on a blockchain of an object distributed ledger.

A method includes a computing device of a computing infrastructure determining to make available for licensing a set of learning objects to produce an object basics record of a smart contract for the set of learning objects. The method further includes verifying, with an accreditation authority computing device of the computing infrastructure, validity of the object basics record. When the object basics record is valid, the method further includes establishing available license terms of the smart contract for the set of learning objects, establishing available payment terms of the smart contract for the set of learning objects, and causing generation of a non-fungible token associated with the smart contract in an object distributed ledger.

A medical image reconstruction system and method for correcting a CT image such that the CT image is located at the center of a three-dimensional (3D) space in order to overcome an error that would otherwise be formed due to an inaccurate position of a patient during Obtaining CT images. After the 3D spatial positions of a 3D medical image are corrected using 3D medical image data, a trajectory of a dental arch is detected. A two-dimensional (2D) medical image is created from the 3D medical image through automatic reconstruction on the basis of the detected trajectory of the dental arch.

Systems, methods, and non-transitory computer-readable media are disclosed for modifying voxel-based 3D representations using 3D digital brush tools and/or resolution filters. For example, the disclosed systems can utilize 3D digital brush tools (e.g., a digital blur brush tool, a digital smudge brush tool, and/or a digital melt brush tool) to identify and modify one or more voxels within a 3D representation using multiple buffers of visual properties. Additionally, the disclosed systems can modify one or more voxels within a 3D representation by rendering the one or more voxels at varying levels of detail using an octree (e.g., a mosaic filter tool). In particular, the disclosed systems can identify one or more voxels within an octree that are smaller than a target voxel size. Moreover, the disclosed systems can combine the identified one or more voxels within the octree to render the 3D representation at varying levels of detail.

A method of calibrating a LiDAR sensor mounted on a vehicle includes storing a reference three-dimensional image acquired by the LiDAR sensor while the LiDAR sensor is in an expected alignment with respect to the vehicle. The reference three-dimensional image includes a first image of a fixed feature on the vehicle. The method further includes, acquiring, using the LiDAR sensor, a three-dimensional image including a second image of the fixed feature, and determining a deviation from the expected alignment of the LiDAR sensor with respect to the vehicle by comparing the second image of the fixed feature in the three-dimensional image to the first image of the fixed feature in the reference three-dimensional image.

A method includes a computing device interpreting a request for a requesting entity to access a set of learning objects pertaining to a common topic represented in a virtual reality environment to produce a set of requested learning object identifiers. The method further includes determining whether a license smart contract for the set of learning objects associated with a non-fungible token of the object distributed ledger affirms access by the requesting entity to the set of learning objects. When access is affirmed, the method furth includes generating the virtual reality environment utilizing a group of object representations in accordance with interaction information for at least some of the object representations of the group of object representations. The method further includes outputting the virtual reality environment to the requesting entity for interactive consumption.