NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis from a collection of posed input images. Like most view synthesis methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images have been processed by a lossy camera pipeline that smooths detail, clips...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 16169 - 16178 |
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01.01.2022
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| ISSN: | 1063-6919 |
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| Abstract | Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis from a collection of posed input images. Like most view synthesis methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images have been processed by a lossy camera pipeline that smooths detail, clips highlights, and distorts the simple noise distribution of raw sensor data. We modify NeRF to instead train directly on linear raw images, preserving the scene's full dynamic range. By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks. In addition to changing the camera viewpoint, we can manipulate focus, exposure, and tonemapping after the fact. Although a single raw image appears significantly more noisy than a postprocessed one, we show that NeRF is highly robust to the zeromean distribution of raw noise. When optimized over many noisy raw inputs (25-200), NeRF produces a scene representation so accurate that its rendered novel views outperform dedicated single and multi-image deep raw denoisers run on the same wide baseline input images. As a result, our method, which we call RawNeRF, can reconstruct scenes from extremely noisy images captured in near-darkness. |
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| AbstractList | Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis from a collection of posed input images. Like most view synthesis methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images have been processed by a lossy camera pipeline that smooths detail, clips highlights, and distorts the simple noise distribution of raw sensor data. We modify NeRF to instead train directly on linear raw images, preserving the scene's full dynamic range. By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks. In addition to changing the camera viewpoint, we can manipulate focus, exposure, and tonemapping after the fact. Although a single raw image appears significantly more noisy than a postprocessed one, we show that NeRF is highly robust to the zeromean distribution of raw noise. When optimized over many noisy raw inputs (25-200), NeRF produces a scene representation so accurate that its rendered novel views outperform dedicated single and multi-image deep raw denoisers run on the same wide baseline input images. As a result, our method, which we call RawNeRF, can reconstruct scenes from extremely noisy images captured in near-darkness. |
| Author | Martin-Brualla, Ricardo Mildenhall, Ben Hedman, Peter Srinivasan, Pratul P. Barron, Jonathan T. |
| Author_xml | – sequence: 1 givenname: Ben surname: Mildenhall fullname: Mildenhall, Ben organization: Google Research – sequence: 2 givenname: Peter surname: Hedman fullname: Hedman, Peter organization: Google Research – sequence: 3 givenname: Ricardo surname: Martin-Brualla fullname: Martin-Brualla, Ricardo organization: Google Research – sequence: 4 givenname: Pratul P. surname: Srinivasan fullname: Srinivasan, Pratul P. organization: Google Research – sequence: 5 givenname: Jonathan T. surname: Barron fullname: Barron, Jonathan T. organization: Google Research |
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| Snippet | Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis from a collection of posed input images. Like most view synthesis methods,... |
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| SubjectTerms | 3D from multi-view and sensors; Computational photography; Low-level vision; Vision + graphics Cameras Dynamic range Pattern recognition Photography Pipelines Rendering (computer graphics) Three-dimensional displays |
| Title | NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images |
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