3D face reconstruction based on position map regression network for Lesion analysis of port wine stains

The evaluation of port wine stain based on three-dimensional information can overcome the inaccuracy of two-dimensional image evaluation methods commonly used in clinic. In this paper, an end-to-end multitasking method is designed for the application of 3D information acquisition of port wine stain. Based on deep learning and position map regression network, the reconstruction from 2D pictures to face 3D point cloud is realized. the facial information of patients with port wine stain is represented by UV position map recording 3D point information of the face, and the dense relationship between 3D points and points with semantic meaning in UV space is characterized with this method. The deep learning network framework based on Encoder-Decoder structure is used to complete unconstrained end-to-end face alignment and 3D face reconstruction, whose parameters are obtained by training the data set with lightweight CNN structure. In the process of neural network training and end-to-end unconstrained image facial reconstruction, each point on the UV position map can be assigned different weights, which can not only be used to improve the network performance in neural network training, but also be used to assign corresponding weights to the focus areas with different disease course in the three-dimensional information reconstruction of the focus area therefore the accuracy of the reconstruction results can be increased. With the help of this method, the three-dimensional reconstruction results can be quickly obtained from a single patient's face image, which can be used for subsequent accurate lesion information analysis and treatment.