Research On 3D Reconstruction Of Image Based On Deep Learning

In recent years,the fields of 3D modeling,3D imaging and computer vision have been developing rapidly,and the technology of recovering 3D models from 2D images has received great attention.Reconstruction 3D models from imaged has important practical application value and development prospect.For example,in the fields of Internet e-commerce,smart cities,cultural relics protection,medical imaging,game production,3D animation,etc.,image-based3 D reconstruction has a wide range of applications.The concept of deep learning comes from the study of artificial neural networks,and deep learning methods have better feature representation capability than traditional methods.In recent years,deep learning has developed rapidly,and the 3D model dataset has become more and more perfect.Therefore,the application of deep learning to the field of 3D reconstruction has also become a current research hotspot.This paper aims to combine deep learning methods with 3D reconstruction technology to optimize the results of 3D reconstruction by improving deep learning networks,and also to extend this technology to practical application areas,providing a new development direction for CAD modeling software.The main work of this paper is as follows.1.This paper adopts a voxel-based 3D reconstruction method,aiming to improve the accuracy of 3D reconstruction results.The reconstruction network structure of "encoderdecoder-refiner" is mainly used to complete the reconstruction task.In order to improve the encoder's ability to extract features from 2D images,the residual network is selected as the backbone of the feature extraction network through comparison experiments,and the structure of the residual module is improved to make the network easier to train and more generalizable;and the attention mechanism is incorporated into the encoder to obtain pixel information from channels and space.In order to optimize the reconstruction results,an optimizer structure is added to the reconstruction network to realize the conversion from coarse model to fine model.Finally,the effectiveness of the method in this paper is verified by comparison experiments.2.In this paper,a mesh-based 3D reconstruction method is improved.To address the problem of missing details in the existing reconstruction network,this paper uses the method of optimizing the VGG16 feature extraction network by adding a jump link similar to the UNet structure in VGG16,which makes the shallow and deep information of the feature extraction network fuse with each other and provides richer information for the cascaded grid deformation network afterwards,so as to achieve the purpose of optimizing the reconstruction results.After that,the effectiveness of this paper's method is verified by comparing it with the existing grid-based reconstruction methods through experiments.3.The feasibility and practicality of the 3D reconstruction algorithm studied in this paper were verified in NX software.The current status of 3D reconstruction function in existing industrial modeling software was first analyzed,and then the 3D reconstruction algorithm of this paper was combined with NX software and developed by relying on NX software.3D reconstruction function buttons and user operation interface were added in NX software,and the results of 3D model reconstruction were shown at the same time,and the functions of fast3 D reconstruction and model preview were realized.