IndustrialNeRF: Accurate 3D Industrial Digital Twin Based on Integrating Neural Radiance Fields Using Unsupervised Learning
Source abstract
Abstract
In the era of Industry 4.0, digital twin technology is revolutionizing traditional manufacturing paradigms. However, the adoption of this technology in modern manufacturing systems is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced X-ray and CT Imaging · Digital Transformation in Industry · Anatomy and Medical Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
Beijing Science and Technology Achievements Transformation Service Center, Beijing 100084, China
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
College of Cyberspace Security, Guangzhou University, Guangzhou 510006, China
Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511466, China
Funding
National Key Research and Development Program of China · National Natural Science Foundation of China
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