VR-NeRF: High-Fidelity Virtualized Walkable Spaces
We present an end-to-end system for the high-fidelity capture, model
reconstruction, and real-time rendering of walkable spaces in virtual reality
using neural radiance fields. To this end, we designed and built a custom
multi-camera rig to densely capture walkable spaces in high fidelity and with
multi-view high dynamic range images in unprecedented quality and density. We
extend instant neural graphics primitives with a novel perceptual color space
for learning accurate HDR appearance, and an efficient mip-mapping mechanism
for level-of-detail rendering with anti-aliasing, while carefully optimizing
the trade-off between quality and speed. Our multi-GPU renderer enables
high-fidelity volume rendering of our neural radiance field model at the full
VR resolution of dual 2Ktimes2K at 36 Hz on our custom demo machine. We
demonstrate the quality of our results on our challenging high-fidelity
datasets, and compare our method and datasets to existing baselines. We release
our dataset on our project website.
6549e5e25d2f6ae0a8ab33d2
Linning Xu
6549e5e25d2f6ae0a8ab33d3
Vasu Agrawal
admin_assigned
2023-11-07T14:32:46.963Z
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Vasu Agrawal
user
vasuagrawal4
6549e5e25d2f6ae0a8ab33d4
William Laney
6549e5e25d2f6ae0a8ab33d5
Tony Garcia
6549e5e25d2f6ae0a8ab33d6
Aayush Bansal
6549e5e25d2f6ae0a8ab33d7
Changil Kim
6549e5e25d2f6ae0a8ab33d8
Samuel Rota Bulò
6549e5e25d2f6ae0a8ab33d9
Lorenzo Porzi
6549e5e25d2f6ae0a8ab33da
Peter Kontschieder
6549e5e25d2f6ae0a8ab33db
Aljaž Božič
6549e5e25d2f6ae0a8ab33dc
Dahua Lin
admin_assigned
2023-11-07T14:42:52.858Z
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Dahua Lin
user
lindahua
6549e5e25d2f6ae0a8ab33dd
Michael Zollhöfer
6549e5e25d2f6ae0a8ab33de
Christian Richardt
claimed_verified
2025-02-13T20:37:01.089Z
67adc0cfe1f8b9375a6c7a63
/avatars/942d92ff9bd9e7a0f8f4687babbb8153.svg
Christian Richardt
user
cr333
VR-NeRF: High-Fidelity Virtualized Walkable Spaces
We present an end-to-end system for the high-fidelity capture, model
reconstruction, and real-time rendering of walkable spaces in virtual reality
using neural radiance fields. To this end, we designed and built a custom
multi-camera rig to densely capture walkable spaces in high fidelity and with
multi-view high dynamic range images in unprecedented quality and density. We
extend instant neural graphics primitives with a novel perceptual color space
for learning accurate HDR appearance, and an efficient mip-mapping mechanism
for level-of-detail rendering with anti-aliasing, while carefully optimizing
the trade-off between quality and speed. Our multi-GPU renderer enables
high-fidelity volume rendering of our neural radiance field model at the full
VR resolution of dual 2Ktimes2K at 36 Hz on our custom demo machine. We
demonstrate the quality of our results on our challenging high-fidelity
datasets, and compare our method and datasets to existing baselines. We release
our dataset on our project website.
The system captures, reconstructs, and renders high-fidelity walkable spaces using neural radiance fields, achieving high-quality real-time VR rendering with a custom dataset and multi-camera rig.
neural radiance fields multi-camera rig HDR appearance perceptual color space mip-mapping level-of-detail rendering anti-aliasing multi-GPU renderer VR resolution volume rendering high-fidelity datasets