The proliferation of technologies, such as extended reality (XR), has increased the demand for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications encompass computer-aided design (CAD), finite element analysis (FEA), scanning, and robotics. However, current methods employed for industrial 3D representations suffer from high implementation costs and reliance on manual human input for accurate 3D modeling. To address these challenges, neural radiance fields (NeRFs) have emerged as a promising approach for learning 3D scene representations based on provided training 2D images. Despite a growing interest in NeRFs, their potential applications in various industrial subdomains are still unexplored. In this paper, we deliver a comprehensive examination of NeRF industrial applications while also providing direction for future research endeavors. We also present a series of proof-of-concept experiments that demonstrate the potential of NeRFs in the industrial domain. These experiments include NeRF-based video compression techniques and using NeRFs for 3D motion estimation in the context of collision avoidance. In the video compression experiment, our results show compression savings up to 48\% and 74\% for resolutions of 1920x1080 and 300x168, respectively. The motion estimation experiment used a 3D animation of a robotic arm to train Dynamic-NeRF (D-NeRF) and achieved an average peak signal-to-noise ratio (PSNR) of disparity map with the value of 23 dB and an structural similarity index measure (SSIM) 0.97.

翻译：扩展现实（XR）等技术的普及，增加了对高质量三维（3D）图形表示的需求。工业3D应用涵盖计算机辅助设计（CAD）、有限元分析（FEA）、扫描及机器人技术。然而，当前用于工业3D表示的方法存在实施成本高、依赖人工手动输入以进行精确3D建模的问题。为应对这些挑战，神经辐射场（NeRFs）作为一种基于提供的训练二维图像学习3D场景表示的前沿方法应运而生。尽管对NeRFs的兴趣日益增长，但其在工业各子领域的潜在应用仍未被充分探索。本文全面审视了NeRF的工业应用，同时为未来研究方向提供了指引。我们还开展了一系列概念验证实验，展示了NeRF在工业领域的潜力，包括基于NeRF的视频压缩技术，以及利用NeRF进行避障场景下的3D运动估计。在视频压缩实验中，我们的结果显示，对于1920×1080和300×168分辨率，压缩率分别高达48%和74%。运动估计实验使用机械臂的3D动画训练了动态神经辐射场（D-NeRF），获得的视差图平均峰值信噪比（PSNR）为23 dB，结构相似性指数（SSIM）为0.97。