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Research map / 3D vision / NeRF and radiance fields

Shape, Pose, and Appearance From a Single Image via Bootstrapped Radiance Field Inversion

CVPR 2023 · 3D vision › NeRF and radiance fields · approach: Dynamic and editable NeRF · 26 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add 30-step hybrid inversion to the encoder initializationOurs · Pascal3D+ CarsIoU: 0.883 -> 0.920Helped
Add 30-step hybrid inversion to the encoder initializationOurs · CUB BirdsFID: 28.15 -> 24.70Helped
Add hybrid inversionOurs · ImageNet categoriesIoUHelped
Add hybrid inversionOurs · ImageNet Airplane and ZebraFID: 18.77 -> 19.78; 21.58 -> 24.47Hurt
Use predicted poses instead of dataset poses (ablation)Ours (N=0) · Pascal3D+ CarsIoU: 0.803 -> 0.883Helped
Use predicted poses instead of dataset poses (ablation)Ours (N=30) · Pascal3D+ CarsIoU: 0.802 -> 0.920Helped
Use predicted poses instead of dataset poses (ablation)Ours · Pascal3D+ CarsFID: 73.20 (16.39) -> 75.90 (15.08); 72.22 (15.10) -> 73.53 (14.36)Mixed
Add path length regularization (ablation)Ours hybrid inversion · Pascal3D+ CarsFIDHelped
Add color mapping (ablation)Ours hybrid inversion · Pascal3D+ CarsPSNRHelped
Use the proposed SDF-based backbone instead of vanilla EG3D (ablation)Ours backbone · ShapeNet ChairsPSNRHelped
Use SDF representation instead of volume density (ablation)Ours generator · Pascal3D+ CarsFIDHelped
Use hybrid inversion instead of the encoder-based approach (ablation)Ours · ShapeNet ChairsPSNRHelped

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