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Research map / 3D vision / NeRF and radiance fields

Is Attention All That NeRF Needs?

ICLR 2023 · 3D vision › NeRF and radiance fields · approach: Few-shot neural rendering architectures · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use epipolar-geometry-constrained view aggregation and learned ray renderingGNT · LLFF (cross-scene generalization)Avg: 0.055 -> 0.047Helped
Use epipolar-geometry-constrained view aggregation and learned ray renderingGNT · NeRF Synthetic (cross-scene generalization)Avg: 0.032 -> 0.029Helped
Use cross-scene-trained GNT for complex opticsGNT · Shiny-6PSNR: 24.12 -> 27.10Helped
Use GNT rather than SRTNovel-view synthesis · NMRPSNR: 27.87 -> 32.12Helped
Use one transformer stage instead of separate view and ray aggregation (ablation)GNT · Drums (Blender)PSNR: 29.41 -> 21.80Hurt
Aggregate epipolar features before aggregating views (ablation)GNT · Drums (Blender)PSNR: 29.41 -> 21.57Hurt
Use dot-product attention rather than subtraction attention in the view transformer (ablation)GNT · Drums (Blender)PSNR: 29.41 -> 26.98Hurt
Use subtraction attention rather than dot-product attention in the view transformer (ablation)GNT · Drums (Blender)SSIM: 0.953 -> 0.931No clear effect
Replace learned ray rendering with classical volumetric rendering (ablation)GNT · Drums (Blender)PSNR: 29.41 -> 24.24Hurt
Add autoregressive ray decoding (ablation)GNT · Orchids (LLFF)PSNR: 20.67 -> 21.05Helped
Add autoregressive ray decoding (ablation)GNT · Horns (LLFF)PSNR: 29.62 -> 28.20Hurt
Add attention-guided fine sampling (ablation)GNT · Orchids (LLFF)PSNR: 20.67 -> 20.69No clear effect

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