Research And Implementation Of 3D Reconstruction Algorithm Using Generative Model

With the increasing demand for 3D reconstruction tasks in the fields of intelligent manufacturing,security monitoring,intelligent driving,video interaction,medical diagnosis,robotics,etc.,the research significance and value of 3D reconstruction from images can be realized.Image-based 3D reconstruction aims to infer 3D models of faces,objects,or scenes in a picture from a single or multiple images,but because the image in the natural environment is affected by objective factors such as light,occlusion,shooting angle,and blurred spatial information Has brought great challenges to the reconstruction work.The 3D reconstruction algorithm has been researched from the beginning to realize the reconstruction from the 3D to 2D projection process to the reconstruction using deep learning technology,and the reconstruction effect has been greatly improved.Deep learning technology has also developed rapidly in recent years.From the initial deep convolutional network to the emergence of new technologies such as generative adversarial networks,reinforcement learning,and deep map convolutional networks,we need to study the application of new technologies in 3D reconstruction tasks.Impact on reconstruction accuracy and reconstruction efficiency.This paper aims to study the application of generative adversarial networks to 3D reconstruction tasks and propose a new 3D reconstruction method.The face reconstruction problem is converted into a problem of generating a three-dimensional model from the image.The powerful learning data distribution ability of the generated model is used to enable the algorithm to solve the problem of affecting reconstruction in a variety of natural environments such as large poses,occlusions,and light.Finally,it can achieve robustness.Face 3D reconstruction algorithm based on a single image.The main content and work of this article are as follows:1.Aiming at the reconstruction of large faces in natural environment,the third chapter of this paper proposes a two-stage reconstruction of image visible model and invisible model based on hierarchical model three-dimensional face reconstruction algorithm(FSGANA)based on the generative model.The first stage of the algorithm reconstructs the visible part model based on the features of the face area in the image.The second stage of the algorithm uses the generation model GAN to generate a three-dimensional model of the invisible part of the image based on the reconstructed visible part model and the face image in the first stage.A complete three-dimensional face model is reconstructed step by step.Experiments show that our reconstruction effect is better than the existing classic face reconstruction algorithms.2.Deep learning networks require a large amount of data for training.The 3D reconstruction task is divided into two stages of reconstruction.Although great improvements have been made in the reconstruction accuracy,the cost of training and testing has greatly increased the time cost.In order to solve both the time and the excellent 3D model generation ability of the generated model,Chapter 4 of this paper proposes an end-to-end face 3D reconstruction algorithm based on the generative model(P2UGAN).The coarse-to-fine generator and UV are designed.The spatial discriminators oppose each other to achieve common optimization,and finally to reconstruct ideal three-dimensional face model.3.In order to integrate three-dimensional reconstruction algorithms,including faces,objects,scenes,etc,to provided online interaction functions.this paper designs and implements a multi-functional 3D experiment system that can integrate multiple 3D reconstruction algorithms,provide online interaction,and access system-level interfaces.