3D dataset generation for neural network model training

A method, comprising:

• in an electronic device:
• receiving a set of two-dimensional (2D) images comprising at least a first two-dimensional (2D) image of an object of interest;
• detecting a plurality of 2D landmarks on the first 2D image, wherein the detected plurality of 2D landmarks corresponds to shape-features of the object of interest;
• aligning a first three-dimensional (3D) shape model of a reference object by fitting corresponding 2D landmarks of the first 3D shape model to the detected plurality of 2D landmarks of the object of interest;
• estimating texture mapping information between the object of interest of the first 2D image and the aligned first 3D shape model;
• generating a dataset by including the first 2D image, the aligned first 3D shape model, and the estimated texture mapping information as a first sample of the dataset;
• training, based on the generated dataset, a neural network model on a task of 3D reconstruction from a single 2D image;
• receiving, by the trained neural network model, a second 2D image of the object of interest as an input, wherein the second 2D image is part of an unseen dataset of images of the object of interest;
• and
• outputting, by the trained neural network model, a second 3D shape model corresponding to the object of interest in the second 2D image, based on the input.

A non-transitory computer-readable medium having stored thereon computer-implemented instructions that, when executed by an electronic device, causes the electronic device to execute operations, the operations comprising:

• receiving a set of two-dimensional (2D) images comprising at least a first two-dimensional (2D) image of an object of interest;
• detecting a plurality of 2D landmarks on the first 2D image, wherein the detected plurality of 2D landmarks corresponds to shape-features of the object of interest;
• aligning a first three-dimensional (3D) shape model of a reference object by fitting corresponding 2D landmarks of the first 3D shape model to the detected plurality of 2D landmarks of the object of interest;
• estimating texture mapping information between the object of interest of the first 2D image and the aligned first 3D shape model;
• generating a dataset by including the first 2D image, the aligned first 3D shape model, and the estimated texture mapping information as a first sample of the dataset;
• training, based on the generated dataset, a neural network model on a task of 3D reconstruction from a single 2D image;
• receiving, by the trained neural network model, a second 2D image of the object of interest as an input, wherein the second 2D image is part of an unseen dataset of images of the object of interest;
• and
• outputting, by the trained neural network model, a second 3D shape model corresponding to the object of interest in the second 2D image, based on the input.