3m Innovative Properties Company

Cyberoptics Corporation

Evan J Ribnick

Guruprasad Somasundaram

Ravishankar Sivalingam

Aya Eid

Shannon D Scott

Golshan Golnari

Anthony J Sabelli

Guillermo Sapiro

Kenneth G Brittain

John A Ramthun

Derek H Justice

Eric P Rudd

Brian J Stankiewicz

Carl E Haugan

Robert D Lorentz

Paul R Haugen

David L Hofeldt

Theresa M Meyer

Gregory D Kostuch

Catherine P Tarnowski

Kevin S Xu

Deepti Pachauri

Arash Sangari

David D Miller

Alexandra R Cunliffe

Benjamin D Zimmer

Sammuel D Herbert

Judy J Ma

Andrzej P Jaworski

Lindsay Labahn

Matthew R Cruikshank

Esa H Vilkama

Mojtaba K Elyaderani

Lindsay L Labahn

A method of determining dimensional information of a target surface includes generating a first point cloud corresponding to a first plurality of reconstructed surface points of the target surface generated by a first imaging system-illumination source pair of a phase profilometry system; generating a second point cloud corresponding to a second plurality of reconstructed surface points of the target surface generated by a second imaging system-illumination source pair of the phase profilometry system; generating an initial estimate of the target surface based on the first and second point clouds; and refining the initial surface estimate using positions of the first and second point clouds and geometry of the first and second imaging system-illumination source pairs to generate a final point cloud.

Three-dimensional sensor with counterposed channels

Methods for recognizing or identifying tooth types using digital 3D models of teeth. The methods include receiving a segmented digital 3D model of teeth and selecting a digital 3D model of a tooth from the segmented digital 3D model. An aggregation of the plurality of distinct features of the tooth is computed to generate a single feature describing the digital 3D model of the tooth. A type of the tooth is identified based upon the aggregation, which can include comparing the aggregation with features corresponding with known tooth types. The methods also include identifying a type of tooth, without segmenting it from an arch, based upon tooth widths and a location of the tooth within the arch.

Method for automatic tooth type recognition from 3D scans

Methods for aligning a digital 3D model of teeth represented by a 3D mesh to a desired orientation within a 3D coordinate system. The method includes receiving the 3D mesh in random alignment and changing an orientation of the 3D mesh to align the digital 3D model of teeth with a desired axis in the 3D coordinate system. The methods can also detect a gum line in the digital 3D model to remove the gingiva from the model.

Method of aligning intra-oral digital 3D models

A method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla, to bring them into bite alignment. The method estimates representative planes for the mandible and maxilla 3D models and transforms the models such that the representative planes are each aligned with their respective coordinate systems and shown in a horizontal view. The method also transforms the models such that the mandible and maxilla are each aligned with a same coordinate system and shown in a front view. The digital 3D models are then brought into bite alignment such that the mandible is in occlusion with the maxilla or within a distance near to occlusion. The digital models of the bite-aligned arch pairs can also be transformed or rotated to be shown centered in a front view.

Automatic alignment and orientation of digital 3D dental arch pairs

A method for detecting tooth wear using digital 3D models of teeth taken at different times. The digital 3D models of teeth are segmented to identify individual teeth within the digital 3D model. The segmentation includes performing a first segmentation method that over segments at least some of the teeth within the model and a second segmentation method that classifies points within the model as being either on an interior of a tooth or on a boundary between teeth. The results of the first and second segmentation methods are combined to generate segmented digital 3D models. The segmented digital 3D models of teeth are compared to detect tooth wear by determining differences between the segmented models, where the differences relate to the same tooth to detect wear on the tooth over time.

Detecting tooth wear using intra-oral 3D scans

Methods for estimating and predicting tooth wear based upon a single 3D digital model of teeth. The 3D digital model is segmented to identify individual teeth within the model. A digital model of a tooth is selected from the segmented model, and its original shape is predicted. The digital model is compared with the predicted original shape to estimate wear areas. A mapping function based upon values relating to tooth wear can also be applied to the selected digital model to predict wear of the tooth.

Estimating and predicting tooth wear using intra-oral 3D scans

Systems and methods for displaying synchronized views and animations of digital 3D models of a person's intra-oral structure such as teeth. Two digital 3D models obtained from scans at different times are displayed in side-by-side views and synchronized via registration of the two scans or corresponding models. A user's control input to one displayed model causes the same manipulation of both models since they are registered. The two digital 3D models can also be displayed in an animation mode where the first model slowly morphs into the second model to illustrate changes in the intra-oral structure over time.

Synchronization and animation of views showing digital 3D models of teeth

A method for characterizing the uniformity of a material includes selecting a set of size scales at which to measure uniformity within an area of interest in an image of the material; suppressing features in the image smaller than a selected size scale of interest within the set of size scales; dividing the image into patches equal to the size scale of interest; and calculating a uniformity value within each patch.

Multiscale uniformity analysis of a material

A computerized inspection system is described for detecting the presence of non-uniformity defects and providing output indicative of a severity of each type of non-uniformity defect. Techniques are described that increase the throughput of the inspection system. Algorithmic and hardware approaches are described to significantly decrease the average amount of time required to inspect a given quantity of material that is expected to be mostly uniform. The techniques described herein involve dynamic selection of which image features to compute by starting with a base feature set and only triggering additional feature computations as needed until the features are sufficient to compute a severity for each type of non-uniformity defect. The number of features extracted and the order in which the features are extracted is dynamically determined in real-time to reduce a cost associated with the feature extraction.

Rapid processing and detection of non-uniformities in web-based materials

A system is described for detecting the presence of non-uniformity patterns and providing output indicative of a severity of each type of non-uniformity pattern. The system includes a computerized rating tool that assists a user in efficiently and consistently assigning expert ratings (i.e., labels) to a large collection of training images representing samples of a given product. In addition, the rating software develops a model that allows a computerized inspection system to detect the presence of non-uniformity patterns in a manufactured web material in real time and provide output indicative of a severity level of each pattern on a continuous scale. The system also includes algorithmic and hardware approaches to significantly that increase the throughput of the inspection system.

System for detection of non-uniformities in web-based materials

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St. Louis Park, MN, United States of America

Evan J Ribnick