English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The proliferation of technologies, such as extended reality (XR), hasincreased the demand for high-quality three-dimensional (3D) graphicalrepresentations. Industrial 3D applications encompass computer-aided design(CAD), finite element analysis (FEA), scanning, and robotics. However, currentmethods employed for industrial 3D representations suffer from highimplementation costs and reliance on manual human input for accurate 3Dmodeling. To address these challenges, neural radiance fields (NeRFs) haveemerged as a promising approach for learning 3D scene representations based onprovided training 2D images. Despite a growing interest in NeRFs, theirpotential applications in various industrial subdomains are still unexplored.In this paper, we deliver a comprehensive examination of NeRF industrialapplications while also providing direction for future research endeavors. Wealso present a series of proof-of-concept experiments that demonstrate thepotential of NeRFs in the industrial domain. These experiments includeNeRF-based video compression techniques and using NeRFs for 3D motionestimation in the context of collision avoidance. In the video compressionexperiment, our results show compression savings up to 48\% and 74\% forresolutions of 1920x1080 and 300x168, respectively. The motion estimationexperiment 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 withthe value of 23 dB and an structural similarity index measure (SSIM) 0.97.

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