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NeRF in the Dark: High Dynamic Range View Synthesis From Noisy Raw Images

CVPR 2022 · 3D vision › NeRF and radiance fields · approach: Few-shot neural rendering architectures · 342 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train NeRF directly on linear raw images rather than tonemapped LDR imagesNeRF · Synthetic Lego with simulated noisesRGB PSNR: 31.25 -> 32.11; 29.14 -> 31.94; 26.10 -> 31.59; 22.31 -> 30.94; 18.27 -> 29.69; 14.87 -> 27.73Helped
Train NeRF directly on linear raw images rather than tonemapped LDR imagesNeRF · Synthetic Lego with clean inputs (infinite shutter speed)sRGB PSNR: 33.16 -> 32.15Hurt
Train on raw images instead of LDR imagesNeRF · Real denoising dataset (3 scenes)Affine-aligned sRGB PSNR: 19.43 -> 23.53Helped
Pre-denoise training images with Unprocess (ablation)RawNeRF · Real denoising dataset (3 scenes)Affine-aligned sRGB PSNR: 23.53 -> 23.35Hurt
Pre-denoise training images with Unprocess (ablation)RawNeRF · Real denoising dataset (3 scenes)Affine-aligned sRGB SSIM: 0.536 -> 0.531Hurt
Pre-denoise training images with Unprocess (ablation)RawNeRF · Real denoising dataset (3 scenes)Affine-aligned sRGB LPIPS: 0.501 -> 0.507No clear effect
Use gradient-reweighted L2 rather than reweighted L1 or the camera-noise negative log-likelihood (ablation)RawNeRF loss · RawNeRF trainingQualitative reconstruction qualityHelped
Use standard unweighted L2 or L1 instead of gradient-reweighted L2 (ablation)RawNeRF loss · Very noisy scenesTraining stabilityHurt
Apply the unclipped sRGB gamma curve directly in the loss (ablation)RawNeRF loss · Dark regionsColor artifactsHurt
Apply the log tone curve directly before L2 rather than reweighting by its gradient (ablation)RawNeRF loss · RawNeRF trainingTraining stabilityHurt
Supervise all color channels using bilinearly demosaicked raw images instead of masking to the measured Bayer channel (ablation)RawNeRF · High-noise image regionsColor artifactsHurt

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