Abstract: 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.

Abstract: A processing device may receive a plurality of intraoral scans of the patient that were generated by an intraoral scanner, determine a three-dimensional surface of at least a portion of one or more dental arch of the patient using the plurality of intraoral scans, automatically determine whether a restorative dental object is represented in at least one of the three-dimensional surface or one or more intraoral scans of the plurality of intraoral scans, and automatically generate a prescription for treating the one or more teeth based at least in part on at least one of a) a presence or absence of a restorative dental object in at least one of the three-dimensional surface or the one or more intraoral scans or b) a location of the restorative dental object in the one or more dental arch of the patient.

Abstract: A processing device receives a plurality of intraoral scans of a dental arch. The processing device determines, from the plurality of intraoral scans, a first set of intraoral scans that depict a restorative object and a second set of intraoral scans that fail to depict any restorative object. The processing device generates a multi-resolution three-dimensional model of the dental arch, wherein a first portion of the multi-resolution three-dimensional model is generated from the first set of intraoral scans, has a first resolution and represents the restorative object, and wherein a second portion of the multi-resolution three-dimensional model is generated from the second set of intraoral scans, has a second resolution and represents a remainder of the dental arch, wherein the first resolution is greater than the second resolution.

Abstract: A method of generating a 3D model includes: determining a 3D surface comprising a preparation tooth using a first plurality of intraoral scans generated by an intraoral scanner at a first time; receiving one or more additional intraoral scans of the preparation tooth that were generated by the intraoral scanner at a second time; determining a change to one or more portions of the preparation tooth between the three-dimensional surface and the one or more additional intraoral scans; determining, based at least in part on the change to the preparation tooth, whether to use a) the 3D surface, b) data from the one or more additional intraoral scans or c) a combination of the 3D surface and the data from the one or more additional intraoral scans to depict one or more portions of the preparation tooth; and generating the 3D model comprising the preparation tooth.

Abstract: A method of intraoral scanning includes receiving a first one or more intraoral scans of a patient's oral cavity; automatically determining, based on processing of the first one or more intraoral scans, a first scanning role associated with the first one or more intraoral scans, wherein the first scanning role is a first one of an upper dental arch role, a lower dental arch role or a bite role; and determining a first three-dimensional surface associated with the first scanning role.

Abstract: In embodiments, a processing device detects a margin line in a model of a preparation from one or more images. The processing device determines, for each segment of a plurality of segments of the margin line, a quality score for the segment. The processing device determines that a segment of the margin line has a quality score that is below a quality threshold, wherein the segment of the margin line was generated based on a first set of images. Responsive to determining that the segment of the margin line has the quality score that is below the quality threshold, the processing devices updates the three-dimensional model of the preparation by replacing a portion of the three-dimensional model associated with the segment of the margin line with image data from at least one of a new image or a second set of images.

Abstract: In embodiments, a processing device generates a three-dimensional model of a dental site from scan data, the three-dimensional model comprising a representation of a tooth, wherein a portion of the three-dimensional model comprises an interfering surface that obscures a portion of the tooth. The processing device receives or generates an image of the tooth, wherein the image depicts the interfering surface. The processing device processes the image to generate a modified image, wherein the portion of the tooth that was obscured by the interfering surface in the image is shown in the modified image. The processing device updates the three-dimensional model of the dental site by replacing, using the modified image, the portion of the three-dimensional model that comprises the interfering surface that obscures the portion of the tooth, wherein the portion of the tooth that was obscured in the three-dimensional model is shown in an updated three-dimensional model.

Abstract: Methods and systems are described that mark and/or correct margin lines and/or other features of dental sites. In one example a three-dimensional model of a dental site is generated from intraoral scan data of the dental site, the three-dimensional model comprising a representation of a preparation tooth. An image of the preparation tooth is received or generated, the image comprising a height map. Data from the image is processed using a trained machine learning model that has been trained to identify margin lines of preparation teeth, wherein the trained machine learning model outputs a probability map comprising, for each pixel in the image, a probability that the pixel depicts a margin line. The three-dimensional model of the dental site is then updated by marking the margin line on the representation of the preparation tooth based on the probability map.