Introduction

International Standard Serial Number:

ISSN 1001-4551

Sponsor:

Zhejiang University;

Zhejiang Machinery and Electrical Group

Edited by:

Editorial of Journal of Mechanical & Electrical Engineering

Chief Editor:

ZHAO Qun

Vice Chief Editor:

TANG ren-zhong,

LUO Xiang-yang

Tel:

86-571-87041360,87239525

Fax:

86-571-87239571

Add:

No.9 Gaoguannong,Daxue Road,Hangzhou,China

P.C:

310009

E-mail:

meem_contribute@163.com

3D reconstruction technology of mixed reality based on neural radiance fields algorithm
Published:2024-10-30 author:DU Xuan, HUANG Yong, DONG Huiliang,et al. Browse: 125 Check PDF documents
3D reconstruction technology of mixed reality 
based on neural radiance fields algorithm


DU Xuan1, HUANG Yong2, DONG Huiliang1, ZHOU Yuhao3, GONG Zheng3, YAO Yulong3, ZENG Xi3

(1.China Tobacco Zhejiang Industrial Co., Ltd., Hangzhou 310008, China; 2.Zhejiang Machinery and 
Electrical Group Co., Ltd., Hangzhou 310002, China; 3.College of Mechanical 
Engineering, Zhejiang University of Technology, Hangzhou 310014, China)


Abstract:  Aiming at the heightened demand for efficiently establishing high-quality three-dimensional (3D)models within mixed reality (MR) environments, a thorough investigation was carried out into the neural radiance fields (NeRF) algorithm-based three-dimensional reconstruction technique. In addition, a dataset optimization algorithm rooted in the Laplacian operator was proposed to augment the overall reconstruction process. Firstly, a 1 min 51 s video around a wire cutting equipment was recorded. Equidistant frame extraction was employed to create a comprehensive training dataset. Then, the Laplacian operator was applied to optimize the dataset, with the original dataset retained for comparative analysis. The NeRF algorithm-based reconstruction method and the traditional dense point cloud reconstruction based on COLMAP method were applied separately to the 3D reconstruction of the two datasets. The reconstruction results, with a focus on precision and speed, were systematically compared across various reconstruction methodologies and datasets. The study results indicate that the reconstruction time of COLMAP dense point cloud is 9.98 times than that of NeRF based reconstruction, and comparing with COLMAP dense point cloud reconstruction, the model surface defects are fewer when using the NeRF reconstruction method. Additionally, the NeRF reconstruction of the dataset optimized by the Laplacian operator has respectively improved the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics by 2.43% and 0.72%, which is beneficial for enhancing the quality of the reconstructed model. This research presents findings that endorse the application of mixed reality technology in the digital transformation of the manufacturing sector, offering valuable insights for pertinent fields.

Key words: highquality threedimensional (3D)models; neural radiance fields (NeRF) algorithm; mixed reality (MR); reconstruction speed; reconstruction accuracy; Laplacian operator; data set optimization algorithm

Advanced Search
Online Office System
  • Chinese Core Periodicals
  • Chinese Sci-tech Core Periodicals
  • SA, INSPEC Indexed
  • CSA: T Indexed
  • UPD:Indexed


2010 Zhejiang Information Institute of Mechinery Industry

Technical Support：Hangzhou Bory science and technology

You are 1895221 visit this site