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Research map / 3D vision / NeRF and radiance fields

AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training

CVPR 2023 · 3D vision › NeRF and radiance fields · approach: Dynamic and editable NeRF · 39 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add convolution-augmented rendering (ablation)mip-NeRF 360 · Bicycle 2560×1680, standard and warped test setsPSNR: 24.17/24.96 -> 24.41/25.20Helped
Add alignment-aware loss to the convolution-augmented model (ablation)mip-NeRF 360 · Bicycle 2560×1680, standard and warped test setsPSNR: 24.41/25.20 -> 24.51/25.45Helped
Add the first-block VGG high-frequency loss (ablation)Mip-NeRF 360 + convolution + alignment · Bicycle 2560×1680, standard and warped test setsPSNR: 24.51/25.45 -> 24.55/25.51Helped
Use iterative optical-flow warping of training images (ablation)mip-NeRF 360, 4096 channels · Flowers 2560×1680, warped test setPSNR: +0.62dBHelped
Use iterative optical-flow warping of training images (ablation)mip-NeRF 360, 1024 channels · Flowers 2560×1680, warped test setPSNR: +0.23dBHelped
Increase the NeRF MLP from 1024 to 4096 channels without correcting alignment (ablation)mip-NeRF 360 · Flowers 2560×1680, standard test setPSNR: 20.83 -> 21.23Helped
Apply AligNeRFmip-NeRF 360 · Outdoor 2560×1680, 1×, standard test setPSNR: 23.71 -> 23.84Helped
Apply AligNeRFmip-NeRF 360 · Outdoor 2560×1680, 4×, warped test setPSNR: 24.83 -> 25.22Helped
Apply AligNeRFmip-NeRF 360 · Outdoor 1280×840PSNR: 24.36 -> 24.55Helped

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