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

Eugen Šlapak, Enrique Pardo+3 more|arXiv (Cornell University)|Aug 14, 2023

Model Reduction and Neural NetworksPhysics and Astronomy3 citations

TL;DR

This paper explores neural radiance fields (NeRFs) for industrial and robotics applications, demonstrating their potential in 3D scene reconstruction, video compression, and dynamic motion estimation. Using proof-of-concept experiments, NeRFs achieved up to 74% video compression savings and 23 dB PSNR with 0.97 SSIM in disparity estimation for robotic motion, highlighting their efficiency and accuracy in industrial settings.

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.

Motivation & Objective

• To investigate the feasibility and benefits of neural radiance fields (NeRFs) in industrial and robotics applications.
• To address high implementation costs and manual input requirements in traditional 3D modeling by leveraging implicit neural representations.
• To demonstrate NeRFs' potential in practical industrial use cases such as video compression and dynamic motion estimation.
• To provide a foundation for future research by identifying unexplored opportunities in industrial NeRF applications.
• To bridge the gap between academic research and industrial deployment through practical proof-of-concept experiments.

Proposed method

• Employed NeRF to learn implicit 3D scene representations from 2D image inputs, mapping 3D coordinates to radiance and density.
• Applied Dynamic-NeRF (D-NeRF) to model time-varying scenes, such as robotic arm movements, by incorporating temporal information.
• Used novel view synthesis and disparity map generation to evaluate depth reconstruction quality in dynamic scenes.
• Conducted video compression experiments by training NeRF on image sequences and reconstructing frames from learned representations.
• Evaluated reconstruction quality using PSNR and SSIM metrics, comparing D-NeRF outputs to ground truth from Blender-rendered animations.
• Visualized data distributions using kernel density estimation and box plots to analyze PSNR and SSIM across training, validation, and test sets.

Experimental results

Research questions

• RQ1 Can NeRFs effectively reduce the cost and manual effort of 3D modeling in industrial applications?
• RQ2 To what extent can NeRFs enable efficient video compression while preserving visual quality in industrial settings?
• RQ3 How accurately can NeRFs estimate 3D motion and depth in dynamic robotic scenes, such as moving arms?
• RQ4 What is the impact of PSNR and SSIM metrics on the practicality of NeRF-based depth estimation for collision avoidance?
• RQ5 What future industrial applications can be enabled by extending NeRFs to multimodal or language-embedded representations?

Key findings

• NeRF-based video compression achieved up to 48% savings at 1920x1080 resolution and 74% savings at 300x168 resolution.
• D-NeRF achieved a mean PSNR of 23 dB and an SSIM of 0.97 when reconstructing disparity maps from a 3D animated robotic arm.
• The D-NeRF model showed lower deviation from ground truth in training sets, indicating robustness in depth reconstruction.
• SSIM values of 0.97–1.0 suggest high-quality reconstruction of large-scale structures, which is sufficient for collision avoidance.
• PSNR was more sensitive to small-scale depth deviations, but such precision is not strictly necessary for robotic safety applications.
• The results indicate that NeRFs can serve as a viable alternative to traditional 3D modeling and rendering in industrial robotics.