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Digital twin systems have been increasingly adopted in the manufacturing industry with recent advancements in IoT, deep learning, and physical AI technologies. These systems are improving productivity for factory automation and process optimization, and are also applicable to safety management within manufacturing environments. In particular, unexpected collisions between workers and robots can be occurred in human-robot collaborative work cell. To prevent the collisions, it is essential to recognize workers in 3D within the workspace and simulate them in a digital twin environment to predict the potential collisions. In this study, we developed a multi-view custom dataset captured with a thermal infrared camera to analyze and even in environments where visibility is impaired by factors such as dust or smoke. We applied video object segmentation and novel view synthesis models to simulate the worker as a 3D point cloud. The performance of models was quantitatively evaluated to identify those best suited for our proposed method. Our results demonstrate that the proposed approach can successfully reconstruct workers as 3D point clouds in robot work cell. This contributes to enhancing the safety and efficiency of smart manufacturing and surveillance applications.
This work was supported in part by Korea Institute of Industrial Technology (KITECH) under Grant JA-25-0003 and Grant EH-26-0002. and This work was supported in part by the Industrial Technology Innovation Program, through the Development of a Near-Field Optimized Camera Image Fusion Ultra-High Resolution LiDAR System under Grant RS-2024-00423589.