US 12,249,031 B2
Method for generating objects using an hourglass predictor
Jens Peter Träff, Copenhagen K (DK); Jens Christian Jørgensen, Seattle, WA (US); Alejandro Alonso Diaz, Copenhagen K (DK); Mathias Bøgh Stokholm, Copenhagen K (DK); and Asger Vejen Hoedt, Vallensbæk (DK)
Assigned to 3SHAPE A/S, Copenhagen K (DK)
Filed by 3SHAPE A/S, Copenhagen K (DK)
Filed on Dec. 20, 2023, as Appl. No. 18/390,173.
Application 18/390,173 is a continuation of application No. 17/434,205, granted, now 11,887,209, previously published as PCT/EP2020/054852, filed on Feb. 25, 2020.
Claims priority of application No. PA 2019 70132 (DK), filed on Feb. 27, 2019.
Prior Publication US 2024/0212278 A1, Jun. 27, 2024
Int. Cl. G06T 17/00 (2006.01); A61C 7/00 (2006.01); A61C 9/00 (2006.01); A61C 13/00 (2006.01); G06T 7/00 (2017.01); G06T 9/00 (2006.01); G06T 11/00 (2006.01); G06T 17/20 (2006.01); G06T 19/20 (2011.01); G06V 10/774 (2022.01); G16H 30/40 (2018.01)
CPC G06T 17/20 (2013.01) [A61C 7/002 (2013.01); A61C 9/0053 (2013.01); A61C 13/0004 (2013.01); A61C 13/0019 (2013.01); G06T 7/0012 (2013.01); G06T 9/002 (2013.01); G06T 11/00 (2013.01); G06T 17/00 (2013.01); G06T 19/20 (2013.01); G06V 10/7747 (2022.01); G16H 30/40 (2018.01); G06T 2207/30036 (2013.01); G06T 2210/41 (2013.01); G06T 2219/2021 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating an object based on output data, comprising
training an autoencoder on a first set of training input data to identify a first set of latent variables and generate first set of output data, where the autoencoder comprises a first encoder, and a first decoder, where the first encoder converts the first set of input data into a first set of latent variables, where the first decoder converts the first set of latent variables to the first set of output data, where the first set of output data is at least substantially the same as the first set of training input data;
training an hourglass predictor to return a second set of latent variables, where the hourglass predictor comprises a second encoder and the first decoder, where the second encoder converts a second set of training input data to the second set of latent variables, where the second set of latent variables has a comparable data format as the first set of latent variables, by the first decoder converts the second set of latent variables into a second set of output data at least substantially the same as a set of training target data, the set of training target data being generally of the same type of underlying object as the first set of training input data, and the second set of training input data is different from the first set of training input data; and
using the hourglass predictor on a third set of input data to generate a third set of output data, where the third set of input data and the second set of input data have the same type of underlying object and the same data format, where the third set of output data is a comparable data format to the first set of output data, and
generating the object based on the third set of output data, wherein the object is a dental restoration, an orthodontic appliance, an ear-related device or a proposed digital 2D image of a desired dental setup based on a pre-treatment digital 2D image.