Publications

@inproceedings{jacoby_efficient_2023,

title = {Efficient Cloud Pipelines for Neural Radiance Fields},

author = {D. Jacoby and D. Xu and W. Ribas and M. Xu and T. Liu and V. Jeyaraman and M. Wei and E. D. Blois and Y. Coady},

editor = {Chakrabarti S. and Paul R.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179765347&doi=10.1109%2fUEMCON59035.2023.10316126&partnerID=40&md5=2640a2b033c9200560f93898a178dbbe},

doi = {10.1109/UEMCON59035.2023.10316126},

isbn = {979-835030413-8 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {IEEE Annu. Ubiquitous Comput., Electron. Mob. Commun. Conf., UEMCON},

pages = {114–119},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {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. © 2023 IEEE.},

keywords = {Azure, Change detection, Cloud analytics, Cloud computing, Cloud-computing, Cluster computing, Containerization, Creatives, Geo-spatial, Multi-views, Neural radiance field, Neural Radiance Fields, Pipelines, User interfaces, Virtual production, Vision communities, Windows operating system},

pubstate = {published},

tppubtype = {inproceedings}

}