SpecNeRF: Gaussian Directional Encoding for Specular Reflections
Neural radiance fields have achieved remarkable performance in modeling the
appearance of 3D scenes. However, existing approaches still struggle with the
view-dependent appearance of glossy surfaces, especially under complex lighting
of indoor environments. Unlike existing methods, which typically assume distant
lighting like an environment map, we propose a learnable Gaussian directional
encoding to better model the view-dependent effects under near-field lighting
conditions. Importantly, our new directional encoding captures the
spatially-varying nature of near-field lighting and emulates the behavior of
prefiltered environment maps. As a result, it enables the efficient evaluation
of preconvolved specular color at any 3D location with varying roughness
coefficients. We further introduce a data-driven geometry prior that helps
alleviate the shape radiance ambiguity in reflection modeling. We show that our
Gaussian directional encoding and geometry prior significantly improve the
modeling of challenging specular reflections in neural radiance fields, which
helps decompose appearance into more physically meaningful components.
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Li Ma
6583bbd225bb2d7767f2048b
Vasu Agrawal
admin_assigned
2023-12-21T09:09:11.080Z
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Vasu Agrawal
user
vasuagrawal4
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Haithem Turki
admin_assigned
2023-12-21T09:09:18.404Z
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Haithem Turki
user
hturki
6583bbd225bb2d7767f2048d
Changil Kim
6583bbd225bb2d7767f2048e
Chen Gao
admin_assigned
2023-12-21T09:09:58.998Z
65049f41c6ae3df8f28cfe96
/avatars/a653ed4a215c4bd34710a1cee9f4d9cd.svg
Chen Gao
user
gaochen315
6583bbd225bb2d7767f2048f
Pedro Sander
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Michael Zollhöfer
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Christian Richardt
claimed_verified
2025-02-13T20:36:54.044Z
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/avatars/942d92ff9bd9e7a0f8f4687babbb8153.svg
Christian Richardt
user
cr333
SpecNeRF: Gaussian Directional Encoding for Specular Reflections
Neural radiance fields have achieved remarkable performance in modeling the
appearance of 3D scenes. However, existing approaches still struggle with the
view-dependent appearance of glossy surfaces, especially under complex lighting
of indoor environments. Unlike existing methods, which typically assume distant
lighting like an environment map, we propose a learnable Gaussian directional
encoding to better model the view-dependent effects under near-field lighting
conditions. Importantly, our new directional encoding captures the
spatially-varying nature of near-field lighting and emulates the behavior of
prefiltered environment maps. As a result, it enables the efficient evaluation
of preconvolved specular color at any 3D location with varying roughness
coefficients. We further introduce a data-driven geometry prior that helps
alleviate the shape radiance ambiguity in reflection modeling. We show that our
Gaussian directional encoding and geometry prior significantly improve the
modeling of challenging specular reflections in neural radiance fields, which
helps decompose appearance into more physically meaningful components.
Gaussian directional encoding and a data-driven geometry prior enhance neural radiance fields for accurate modeling of view-dependent glossy surfaces under complex near-field lighting.
neural radiance fields view-dependent appearance glossy surfaces near-field lighting Gaussian directional encoding prefiltered environment maps preconvolved specular color roughness coefficients geometry prior shape radiance ambiguity reflection modeling