Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases

TL;DR

This paper assesses the potential of neural radiance fields (NeRFs) in industrial and robotics contexts, arguing that NeRFs can reduce 3D modeling costs and enable richer scene representations. It surveys derivative NeRF variants (for sample efficiency, rendering speed, and dynamic scenes) and maps their applicability to CAE, SCADA, training, navigation, and 3D scanning. The authors validate NeRF viability with two proof‑of‑concepts: NeRF‑based UAV video compression achieving notable bitrate savings, and D‑NeRF–driven disparity maps for obstacle avoidance. The work highlights practical integration strategies (e.g., edge computing, scene modification with CodeNeRF/CLIP‑NeRF) and outlines concrete directions for future research, including predictive frame generation and language‑guided NeRFs, to bridge academia and industry.

Abstract

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.

Figures (10)

• Figure 1: (a) A 5D coordinate (spatial location in 3D coupled with directional polar angles) is transformed into a higher-dimensional space by positional encoding $\gamma$. It serves as an input for MLP $F_{W}$. The output from $F_{W}$ consists of gradually learned color $\hat{\boldsymbol{c}}$ and volume density $\sigma$ for the corresponding 5D input coordinate. $\boldsymbol{r}$, $\boldsymbol{d}$, $\boldsymbol{x}$ denote the ray vector, direction vector and spatial location vector, respectively. (b) Pixel values are obtained via volume rendering with numerically integrated rays bounded by respective near and far bounds $t_n$ and $t_f$. (c) Ground truth pixels from training set images are used to calculate rendering loss and optimize the $F_{W}$ weights via backpropagation mildenhall2021nerf.
• Figure 2: (a) A UAV camera captures the environment. The real frame and pose of the camera are transmitted wirelessly to a nearby multiaccess edge computing (MEC) server. (b) The MEC server employs the NeRF model for novel view synthesis based on camera pose. The H.264 codec encodes real and NeRF frames to obtain P frame containing their differences, which is transferred through the network with the pose. (c) Receiver rebuilds the real frame using H.264 codec from P frame and locally generated NeRF frame from camera pose.
• Figure 3: A UAV camera captures images at points illustrated on the dashed UAV trajectory. Images are indexed from 0 to 158, and this indexing is also used in Figs. 5-7. The frame with index 10 has the worst SSIM, as shown in Fig. \ref{['fig:ssim']}. Cropped areas from this frame, with their reconstruction by NeRF, are shown to highlight the causes of SSIM degradation.
• Figure 4: NeRF frame reconstruction quality along the UAV trajectory through the 3D scene measured with PSNR in dB.
• Figure 5: NeRF frame reconstruction quality along the UAV trajectory through the 3D scene measured with SSIM.