A computer-implemented method includes receiving a 3D model representative of upper teeth (U1) of a patient (P) and receiving a plurality of images of a face of the patient (P). The method also includes generating a facial model (200) of the patient based on the received plurality of images and determining, based on the determined facial model (200), the received 3D model of 10 the upper teeth (U1) and the plurality of images, a spatial relationship between the upper teeth (U1) of the patient (P) and a facial skeleton of the patient (P).

The present invention relates to a method for deriving a 3D data from image data comprising: receiving, from at least one camera, image data representing an environment; detecting, from the image data, at least one object within the environment; classifying the at least one detected object, wherein the method comprises, for each classified object of the classified at least one objects: determining a 2D skeleton of the classified object by implementing a neural network to identify features of the classified object in the image data corresponding to the classified object; and constructing a 3D skeleton for the classified object, comprising mapping the determined 2D skeleton to 3D.

A computer-implemented method includes receiving a 3D model of upper teeth (U1) of a patient (P) and a 3D model of lower teeth (L1) of the patient (P1), and receiving a plurality of 2D images, each image representative of at least a portion of the upper teeth (U1) and lower teeth (L1) of the patient (P). The method also includes determining, based on the 2D images, a spatial relationship between the upper teeth (U1) and lower teeth (L1) of the patient (P).

A computer-implemented method includes receiving a 3D model of upper teeth (U1) of a patient (P) and a 3D model of lower teeth (L1) of the patient (P1), and receiving a plurality of 2D images, each image representative of at least a portion of the upper teeth (U1) and lower teeth (L1) of the patient (P). The method also includes determining, based on the 2D images, a spatial relationship between the upper teeth (U1) and lower teeth (L1) of the patient (P).

Systems, methods, and computer-readable media for using full waveform inversion for imaging surveyed mediums are provided. The full waveform inversion uses a Sifrian functional to fully leverage Hessian information and update a model by augmenting and assembling data derived from the Sifrian functional when equilibrated. The Sifrian inversion produces high resolution images of the surveyed medium typically only seen with full Hessian inversions and can produce such images without requiring supercomputer computation power or extremely long computation time.

Methods and apparatuses for asynchronously identifying and modeling a gingiva strip from the three-dimensional (3D) dental model of the patient's dentition. These methods may reduce the time required to generate accurate 3D dental models and therefore may reduce and streamline the process of generating dental treatment plans.

There are provided techniques for analyzing an atom probe tomography data set obtained from a tip-shaped sample. The techniques include defining analysis sub-volumes in the atom probe tomography data set; performing a fast Fourier transform (FFT) on each of the analysis sub-volumes to obtain a signal in a Fourier domain; identifying at least one FFT peak in the signal in the Fourier domain, each FFT peak being indicative of an expected crystal feature in the corresponding analysis sub-volume; continuously and automatically calculating an image compression factor and a radius of the tip-shaped sample, based on identified crystal features, the identified crystal features being obtained from a collection of expected crystal features; and reconstructing a three-dimensional model of the tip-shaped sample. Said reconstructing includes comparing the identified crystal features with calibration data; and dynamically adjusting the image compression factor and the radius of the tip-shaped sample.

There are provided techniques for analyzing an atom probe tomography data set obtained from a tip-shaped sample. The techniques include defining analysis sub-volumes in the atom probe tomography data set; performing a fast Fourier transform (FFT) on each of the analysis sub-volumes to obtain a signal in a Fourier domain; identifying at least one FFT peak in the signal in the Fourier domain, each FFT peak being indicative of an expected crystal feature in the corresponding analysis sub-volume; continuously and automatically calculating an image compression factor and a radius of the tip-shaped sample, based on identified crystal features, the identified crystal features being obtained from a collection of expected crystal features; and reconstructing a three-dimensional model of the tip-shaped sample. Said reconstructing includes comparing the identified crystal features with calibration data; and dynamically adjusting the image compression factor and the radius of the tip-shaped sample.

An intraoral scanning system includes an intraoral scanner and a computing device. The computing device receive a plurality of intraoral scans from the intraoral scanner during an intraoral scanning session; registers the plurality of intraoral scans together based on overlapping features of the plurality of intraoral scans; generates a first three-dimensional (3D) surface based on the plurality of intraoral scans; receives one or more additional intraoral scans; determines that the one or more additional intraoral scans fail to satisfy one or more registration criteria for registering to at least one of the plurality of intraoral scans or the first 3D surface; and generates a second 3D surface based on the one or more additional intraoral scans.

Systems and methods are described for creating three dimensional models of building objects by creating a point cloud from a plurality of input images, defining edges of the building object's surfaces represented by the point cloud, creating simplified geometries of the building object's surfaces and constructing a building model based on the simplified geometries. Input images may include ground, orthographic, or oblique images. The resultant model may be scaled according to correlation with select image types and textured.