A system and method that is a web based application for creating and managing orthodontic laboratory prescriptions within a dental clinic or lab. The system receives a standard triangle language (STL) image file which has been obtained from an intraoral digital scan or cone-beam of a patient's mouth and teeth. The user manually selects brackets, attachments, bands, or other appliances and removes them from the 3D image file via a series of selections made through an interface within a digital workspace. The system then calculates a surface of the tooth that was disposed beneath the removed appliance and integrates it into the 3D image to create a clean 3D image of the patient's teeth free from any appliances. After the appliances have been removed, the user may further refine the image of the patient's teeth using a suite of tools including addition, deletion, smoothening, and definition tools.

Methods and apparatuses for performing image processing using artificial intelligence are provided. In an embodiment, an image processing method includes acquiring a target image based on an editing operation on an original image, performing a filling processing operation on the target image to obtain a first filled image including a first target region, identifying a target patch based on the first filled image, calculating a similarity value between the target patch and at least one patch related to the first filled image using a first artificial intelligence (AI) model, determining a target residual patch corresponding to the target patch based on the similarity value and generating a processing result image based on the target residual patch.

A method, computer-readable storage medium, and device for generating a character model. The method comprises: receiving an input image of a reference subject; processing the input image to generate a normalized image; identifying a set of features present in the normalized image, wherein each feature in the set of features corresponds to a portion of a head or body of the reference subject; for each feature in the set of features, processing at least a portion of the normalized image including the feature by a neural network model corresponding to the feature to generate a parameter vector corresponding to the feature; and combining the parameter vectors output by respective neural network models corresponding to respective features in the set of features to generate a parameterized character model corresponding to reference subject in the input image.

A method for fabricating a custom cranial remodeling device for correction of cranial deformities in a subject is described. The method comprises generating a three-dimensional head data file for the subject and determining contour lines on the head. The method further comprises automatically generating a modified head shape data file and juxtaposing the modified head shape with the head represented by the three-dimensional head data file having the contour lines thereon. Still further the method includes utilizing the modified head shape data file to generate a shape for a desired custom cranial remodeling device, the shape having an interior surface to contact the head and an outer surface. The method also includes projecting lines outward from the contour lines to the outer surface and utilizing the projected lines to establish cranial remodeling device contour lines for the custom cranial remodeling device.

In a video capture system, a virtual lens is simulated when applying a crop or zoom effect to an input video. An input video frame is received from the input video that has a first field of view and an input lens distortion caused by a lens used to capture the input video frame. A selection of a sub-frame representing a portion of the input video frame is obtained that has a second field of view smaller than the first field of view. The sub-frame is processed to remap the input lens distortion to a desired lens distortion in the sub-frame. The processed sub-frame is the outputted.

A method, computer program, and computer system for separating a target voice from among a plurality of speakers is provided. Video data associated with the plurality of speakers and audio data associated with each of the one or more speakers are received. Video feature data is extracted from the received video data. The target voice is identified from among the plurality of speakers based on the received audio data and the extracted video feature data.

Systems, computer-implemented methods, and computer program products to facilitate automatic multi-dimensional model generation and tracking in an augmented reality environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a label component that annotates a multi-dimensional point cloud representation of an object present in augmented reality data. The computer executable components further comprise a content generation component that generates a multi-dimensional model of a component of the object based on the multi-dimensional point cloud representation.

A method is provided for generating a visualization for explaining a behavior of a machine learning (ML) model, the method includes inputting an image into a machine learning (ML) model for an inference operation. A first heatmap is generated for the image using a first visualization method. An area of highest attention is selected on the first heatmap based a predetermined threshold. The selected area is cropped from the image. The cropped selected area is upscaled. A second heatmap is generated for the cropped and upscaled selected area of the image. A final visualization is presented for analysis. In another embodiment, a computer program comprising instructions for executing the method is provided.

Disclosed is a mobile terminal that provides an augmented reality navigation screen in a state of being hold in a vehicle, the mobile terminal including: at least one camera configured to obtain a front image; a display; and at least one processor configured to calibrate the front image, and to drive an augmented reality navigation application so that the augmented reality navigation screen including at least one augmented reality (AR) graphic object and the calibrated front image is displayed on the display.

A method of limiting processing by a 3D reconstruction system of an environment in a 3D reconstruction of an event includes dividing by the subdivision module the volume into sub-volumes; projecting from each camera each of the sub-volumes to create a set of sub-volumes masks relative to each camera; creating an imaging mask for each camera; comparing for each camera by the subdivision module the respective imaging mask to the respective sub-volume mask and extracting at least one feature from at least one imaging mask; saving by the subdivision module the at least one feature to a subspace division mask; cropping the at least one feature from the imaging frames using the subspace division mask; and processing only the at least one feature for a 3D reconstruction. The system includes cameras for recording the event in imaging frames; and a subdivision module for dividing the volume into sub-volumes.