Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields

Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location. While NeRF-based techniques excel at represen...

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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 5481 - 5490
Hauptverfasser: Verbin, Dor, Hedman, Peter, Mildenhall, Ben, Zickler, Todd, Barron, Jonathan T., Srinivasan, Pratul P.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2022
Schlagworte:
3D from multi-view and sensors; Vision + graphics
Computer vision
Graphics
Multilayer perceptrons
Pattern recognition
Reflection
Sensors
Three-dimensional displays
ISSN:1063-6919
Online-Zugang:Volltext
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Zusammenfassung:Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location. While NeRF-based techniques excel at representing fine geometric structures with smoothly varying view-dependent appearance, they often fail to accurately capture and reproduce the appearance of glossy surfaces. We address this limitation by introducing Ref-NeRF, which replaces NeRF's parameterization of view-dependent outgoing radiance with a representation of reflected radiance and structures this function using a collection of spatially-varying scene properties. We show that together with a regularizer on normal vectors, our model significantly improves the realism and accuracy of specular reflections. Furthermore, we show that our model's internal representation of outgoing radiance is interpretable and useful for scene editing.
ISSN:1063-6919
DOI:10.1109/CVPR52688.2022.00541