## 1 Introduction  
Digital twins in the industrial realm serve as a vital bridge linking the physical and digital worlds within manufacturing systems. This innovative approach paves the way for real-time data exchange, monitoring, and advanced analytics, which are crucial for Industry 4.0 practices [1]. Among these, the three-dimensional (3D) digital twin models stand out for their significance in creating a holistic representation of products and processes. These 3D models play an instrumental role in various industrial applications, especially in the areas of product design, simulation, testing, and production [2, 3]. By providing a comprehensive digital representation of a physical asset, these models enable businesses to make better-informed decisions, reduce system downtime, and enhance product quality [4]. These models not only offer engineers and designers an intuitive tool that facilitates observing and refining designs in a virtual setting but also present the entire production team with a unified reference. Such references are invaluable in ensuring precision and consistency throughout the actual production process, leading to optimized workflows and improved product lifecycles [5, 6]. Digital twin technology has also been applied to underground utility tunnels for disaster management and infrastructure maintenance, demonstrating its versatility and effectiveness in diverse applications [7, 8]. Moreover, integrating digital twin models with other technologies like artificial intelligence and machine learning can further enhance predictive maintenance, resource optimization, and product customization [9]. In essence, 3D digital twin models herald immense opportunities for next-generation manufacturing systems, steering the industry towards a future characterized by smart, sustainable, and efficient practices [10; 11]. However, the adoption of digital twin technology in current manufacturing systems encounters numerous challenges. One of the primary impediments stems from the conservative nature of manufacturing enterprises, which results in a scarcity of labeled data. This limitation hinders the application of state-of-the-art supervised machine learning algorithms known for their remarkable performance [12]. Additionally, the strong dependency of deep learning models on training datasets and their limited generalization pose significant problems. These models require the operational scenario to align closely with the data distribution from which they were trained [13]. In manufacturing systems, substantial data heterogeneity exists due to variations between different factories or even equipment, leading to exorbitant costs for data recollection. Such costs significantly obstruct the widespread adoption of deep learning models in real-world manufacturing applications [14]. Furthermore, with the advent of sophisticated technologies such as autonomous driving, robotics, and advanced automation systems in the manufacturing domain [15], there is an escalating demand for 3D digital twin models to achieve higher precision and real-time generation. Thus, there is an urgent need to develop models capable of automatic data labeling or those that can operate through unsupervised learning [16]. Enhancing the utility and generalization of models using unsupervised learning, and improving their accuracy and computational speed, remain focal points for both the academic and industrial communities. In this paper, we introduce **IndustrialNeRF**, a novel approach that leverages neural radiance fields to achieve unsupervised 3D reconstruction from 2D images, significantly reducing the cost of generating digital twins in industrial settings. Our method addresses the critical challenge of data scarcity in manufacturing by eliminating the dependency on labeled datasets, thereby streamlining the creation of digital twin models. By autonomously reconstructing precise 3D models from high-resolution images, IndustrialNeRF enhances the efficiency and accuracy of digital twin generation.