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Research map / 3D vision / NeRF and radiance fields

DyLiN: Making Light Field Networks Dynamic

CVPR 2023 · 3D vision › NeRF and radiance fields · approach: Inverse rendering with neural fields · 23 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Distill a dynamic ray-based light field student from a pretrained dynamic NeRF, then fine-tune on real videoDyLiN · Synthetic dynamic scenesLPIPS: 0.0663 -> 0.0184Helped
Use DyLiN instead of TiNeuVox-BSynthetic dynamic scenes · Synthetic Lego sceneWall-clock time (ms): 6,920.0 -> 116.0Helped
Add controllable attribute inputs and attribute-specific masked hyperspace codesCoDyLiN · Eyes/MouthMS-SSIM: 0.7458 -> 0.9510Helped
Add controllable attribute inputs and attribute-specific masked hyperspace codesCoDyLiN · TransformerPSNR: 23.0319 -> 23.5882Helped
Remove deformation and hyperspace MLPs (ablation)DyLiN · Synthetic dynamic scenesPSNR: 32.07 -> 32.43Hurt
Replace explicit ray deformation and ray hyperspace MLPs with pointwise deformation MLP only (ablation)DyLiN · Synthetic dynamic scenesPSNR: 31.24 -> 32.43Hurt
Remove fine-tuning on real data (ablation)DyLiN · Synthetic dynamic scenesPSNR: 31.37 -> 32.43Hurt
Remove fine-tuning on real data (ablation)DyLiN · Real dynamic scenesPSNR: 24.0 -> 25.1Hurt
Use between 4 and 32 sampled points per ray rather than a larger number (ablation)DyLiN · StandupPSNRNo clear effect
Increase ray samples per ray to 64 or 128 (ablation)DyLiN · StandupPSNRHurt
Use smaller KD sample counts, from 100 through 5000 (ablation)DyLiN · StandupPSNRHurt

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