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 a structural similarity index measure (SSIM) 0.97.

中文翻译：

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工业和机器人领域的神经辐射场：应用、研究机会和用例


扩展现实 (XR) 等技术的激增增加了对高质量三维 (3D) 图形表示的需求。工业 3D 应用包括计算机辅助设计 (CAD)、有限元分析 (FEA)、扫描和机器人技术。然而，当前用于工业 3D 表示的方法存在实施成本高以及依赖人工输入来实现精确 3D 建模的问题。为了应对这些挑战，神经辐射场 (NeRF) 已成为一种基于提供的训练 2D 图像学习 3D 场景表示的有前途的方法。尽管人们对 NeRF 的兴趣日益浓厚，但它们在各种工业子领域的潜在应用仍有待探索。在本文中，我们对 NeRF 工业应用进行了全面检查，同时也为未来的研究工作提供了方向。我们还提出了一系列概念验证实验，展示了 NeRF 在工业领域的潜力。这些实验包括基于 NeRF 的视频压缩技术以及在避免碰撞的情况下使用 NeRF 进行 3D 运动估计。在视频压缩实验中，我们的结果显示，对于 1920x1080 和 300x168 的分辨率，压缩率分别高达 48% 和 74%。运动估计实验使用机械臂的3D动画来训练Dynamic-NeRF（D-NeRF），并获得视差图的平均峰值信噪比（PSNR）值为23 dB和结构相似指数测量（SSIM）0.97。