% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Li:632206,
      author       = {Li, Ke and Schmidt, Susanne and Rolff, Tim and Bacher,
                      Reinhard and Leemans, Wim and Steinicke, Frank},
      title        = {{I}nteractive {D}ata {F}usion of {N}eural {R}adiance
                      {F}ields for {I}ndustrial {F}acility {I}nspection in
                      {V}irtual {R}eality},
      address      = {Piscataway, NJ},
      publisher    = {IEEE},
      reportid     = {PUBDB-2025-02134},
      isbn         = {979-8-3503-7449-0},
      pages        = {815 - 816},
      year         = {2024},
      note         = {Literaturangaben; "The 31st IEEE Conference on Virtual
                      Reality and 3D User Interfaces" - Konferenz - Homepage;},
      comment      = {[Ebook] 2024 IEEE Conference on Virtual Reality and 3D User
                      Interfaces Abstracts and Workshops : VRW 2024 : 16-21 March
                      2024, Orlando, Florida : proceedings / , Piscataway, NJ :
                      IEEE, 2024,},
      booktitle     = {[Ebook] 2024 IEEE Conference on
                       Virtual Reality and 3D User Interfaces
                       Abstracts and Workshops : VRW 2024 :
                       16-21 March 2024, Orlando, Florida :
                       proceedings / , Piscataway, NJ : IEEE,
                       2024,},
      abstract     = {Real-world industrial facilities often present complex
                      equipment that requires extensive inspection and maintenance
                      for their operations. In this work, we present a virtual
                      reality (VR) framework that supports virtual facility
                      inspection and maintenance tasks by using neural radiance
                      fields (NeRF) models to replicate, store, and visualize the
                      appearance of complex industrial facilities. To overcome the
                      performance bottleneck of VR NeRF rendering, we present two
                      novel interactive data fusion techniques that can merge a
                      NeRF model with its' corresponding CAD model through a mixed
                      reality tunneling effect and contextual 3D NeRF drawing
                      interaction. Technical benchmarking results and preliminary
                      expert feedback are presented for the initial evaluation of
                      our framework.},
      month         = {Mar},
      date          = {2024-03-16},
      organization  = {2024 IEEE Conference on Virtual
                       Reality and 3D User Interfaces
                       Abstracts and Workshops, Orlando (USA),
                       16 Mar 2024 - 21 Mar 2024},
      cin          = {M},
      cid          = {I:(DE-H253)M-20120731},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz
                      Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2019_IVF-HIDSS-0002$},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1109/VRW62533.2024.00204},
      url          = {https://bib-pubdb1.desy.de/record/632206},
}