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Neural 3D Video Synthesis From Multi-View Video

CVPR 2022 · 3D vision › NeRF and radiance fields · approach: Inverse rendering with neural fields · 380 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use DyNeRF instead of NeRF-T10-second sequence rendering · 10-second sequencePSNR: 28.4487 -> 29.5808Helped
add hierarchical training and importance sampling (ablation)DyNeRF · 10-second sequencePSNR: 28.4994 -> 29.5808Helped
add hierarchical training and importance sampling (ablation)DyNeRF · 10-second sequenceLPIPS: 0.0985 -> 0.0832Helped
use global-median importance sampling (ablation)DyNeRF · 10-second sequencePSNR: 28.4994 -> 29.4623Helped
use temporal-difference importance sampling (ablation)DyNeRF · 10-second sequencePSNR: 28.4994 -> 29.7161Helped
combine global-median and temporal-difference sampling (ablation)DyNeRF · 10-second sequenceLPIPS: 0.0985 -> 0.0832Helped
increase latent code dimension from 8 to 1,024 (ablation)DyNeRF · 60 consecutive framesPSNR: 26.4349 -> 27.6286Helped
increase latent code dimension from 1,024 to 8,192 (ablation)DyNeRF · 60 consecutive framesPSNR: 27.6286 -> 27.4100Hurt
use DyNeRF instead of NeuralVolumesnovel-view rendering · 10-second sequencePSNR: 22.7975 -> 29.5808Helped
use DyNeRF instead of LLFFnovel-view rendering · 10-second sequencePSNR: 23.2388 -> 29.5808Helped
use DyNeRF instead of NeRF-Tvideo perceptual quality · 10-second sequenceJOD: 7.73 -> 8.07Helped
freeze keyframe latent codes during training (ablation)DyNeRF latent-code optimization · rendering qualitysharpnessHurt

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