3D DATASET GENERATION FOR NEURAL NETWORK MODEL TRAINING

摘要

An electronic device receives a set of 2D images comprising at least first 2D image of an object of interest and detects a plurality of 2D landmarks on the first 2D image. The detected plurality of 2D landmarks corresponds to shape-features of the object of interest. The electronic device aligns a first three-dimensional (3D) shape model of a reference object by fitting 2D landmarks of the first 3D shape model to the detected plurality of 2D landmarks. The electronic device estimates texture mapping information between the object of interest and the aligned first 3D shape model and generates 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. Based on the generated dataset, the electronic device trains a neural network model on a task of 3D reconstruction from a single 2D image.