HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces
Neural radiance fields provide state-of-the-art view synthesis quality but
tend to be slow to render. One reason is that they make use of volume
rendering, thus requiring many samples (and model queries) per ray at render
time. Although this representation is flexible and easy to optimize, most
real-world objects can be modeled more efficiently with surfaces instead of
volumes, requiring far fewer samples per ray. This observation has spurred
considerable progress in surface representations such as signed distance
functions, but these may struggle to model semi-opaque and thin structures. We
propose a method, HybridNeRF, that leverages the strengths of both
representations by rendering most objects as surfaces while modeling the
(typically) small fraction of challenging regions volumetrically. We evaluate
HybridNeRF against the challenging Eyeful Tower dataset along with other
commonly used view synthesis datasets. When comparing to state-of-the-art
baselines, including recent rasterization-based approaches, we improve error
rates by 15-30% while achieving real-time framerates (at least 36 FPS) for
virtual-reality resolutions (2Kx2K).
657138bf670035a607452ee7
Haithem Turki
admin_assigned
2023-12-07T11:59:31.113Z
656e000253703dd78fd072a9
/avatars/6702ba8fabe3d08884aa757f90cea333.svg
Haithem Turki
user
hturki
657138bf670035a607452ee8
Vasu Agrawal
admin_assigned
2023-12-07T11:59:38.453Z
6527fa0f763bcce52dfb1af7
/avatars/79bdaa7f1948dfec4d9b6139952fbfe1.svg
Vasu Agrawal
user
vasuagrawal4
657138bf670035a607452ee9
Samuel Rota Bulò
657138bf670035a607452eea
Lorenzo Porzi
657138bf670035a607452eeb
Peter Kontschieder
657138bf670035a607452eec
Deva Ramanan
admin_assigned
2023-12-07T11:59:57.707Z
6337151b0267ebcf02640eb6
/avatars/14a723cafc5587043bdfb19304fc202d.svg
Deva Ramanan
user
devakramanan
657138bf670035a607452eed
Michael Zollhöfer
657138bf670035a607452eee
Christian Richardt
claimed_verified
2025-02-13T20:36:57.610Z
67adc0cfe1f8b9375a6c7a63
/avatars/942d92ff9bd9e7a0f8f4687babbb8153.svg
Christian Richardt
user
cr333
HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces
Neural radiance fields provide state-of-the-art view synthesis quality but
tend to be slow to render. One reason is that they make use of volume
rendering, thus requiring many samples (and model queries) per ray at render
time. Although this representation is flexible and easy to optimize, most
real-world objects can be modeled more efficiently with surfaces instead of
volumes, requiring far fewer samples per ray. This observation has spurred
considerable progress in surface representations such as signed distance
functions, but these may struggle to model semi-opaque and thin structures. We
propose a method, HybridNeRF, that leverages the strengths of both
representations by rendering most objects as surfaces while modeling the
(typically) small fraction of challenging regions volumetrically. We evaluate
HybridNeRF against the challenging Eyeful Tower dataset along with other
commonly used view synthesis datasets. When comparing to state-of-the-art
baselines, including recent rasterization-based approaches, we improve error
rates by 15-30% while achieving real-time framerates (at least 36 FPS) for
virtual-reality resolutions (2Kx2K).
HybridNeRF improves view synthesis quality and efficiency by combining surface and volumetric representations, achieving real-time performance while reducing error rates.
neural radiance fields volume rendering surface representations signed distance functions ray view synthesis Eyeful Tower dataset rasterization-based approaches real-time framerates