Efficient Cloud Pipelines for Neural Radiance Fields
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Abstract (arXiv)
Since their introduction in 2020, Neural Radiance Fields (NeRFs) have taken the computer vision community by storm. They provide a multi-view representation of a scene or object that is ideal for eXtended Reality (XR) applications and for creative endeavors such as virtual production, as well as change detection operations in geospatial analytics. The computational cost of these generative AI models is quite high, however, and the construction of cloud pipelines to generate NeRFs is neccesary to realize their potential in client applications. In this paper, we present pipelines on a high performance academic computing cluster and compare it with a pipeline implemented on Microsoft Azure. Along the way, we describe some uses of NeRFs in enabling novel user interaction scenarios.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 3 Nov 2023.
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8 of 16 references.
References · 8
- 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 398
- 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 311
- 2021BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Renderingcited by 229
- 2022NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)cited by 158
- 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
- 2021No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Modelscited by 50
- 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 50
- 2022Real-Time Neural Light Field on Mobile Devicescited by 48
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