Hidden
NeRF — Hidden Tier
(5 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| camera_pose | -0.7 – 2.3 | mm/deg |
| focal_length | -3.5 – 11.5 | pixels |
| distortion | -0.007 – 0.023 | |
| exposure | -7.0 – 23.0 | % |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | NeRFactor2 + gradient | 0.674 | 26.62 | 0.849 | 0.84 | ✓ Certified | Barron et al., NeurIPS 2024 |
| 2 | 3D-GS++ + gradient | 0.671 | 27.09 | 0.86 | 0.78 | ✓ Certified | Kerbl et al., SIGGRAPH 2024 |
| 3 | COLMAP+MVS + gradient | 0.613 | 24.21 | 0.776 | 0.8 | ✓ Certified | Schonberger & Frahm, CVPR 2016 |
| 4 | GaussianShader + gradient | 0.603 | 23.34 | 0.744 | 0.86 | ✓ Certified | Wang et al., ICCV 2024 |
| 5 | Instant-NGP + gradient | 0.600 | 23.68 | 0.757 | 0.8 | ✓ Certified | Muller et al., SIGGRAPH 2022 |
| 6 | 2DGS + gradient | 0.585 | 23.18 | 0.738 | 0.79 | ✓ Certified | Huang et al., CVPR 2024 |
| 7 | NeRF + gradient | 0.580 | 22.37 | 0.706 | 0.87 | ✓ Certified | Mildenhall et al., ECCV 2020 |
| 8 | 3D-GS + gradient | 0.563 | 22.19 | 0.698 | 0.81 | ✓ Certified | Kerbl et al., SIGGRAPH 2023 |
| 9 | Photogrammetry + gradient | 0.562 | 22.61 | 0.715 | 0.75 | ✓ Certified | Structure-from-Motion baseline |
| 10 | Mesh-GS + gradient | 0.419 | 17.55 | 0.478 | 0.76 | ✓ Certified | Li et al., ECCV 2024 |
| 11 | Mip-NeRF 360 + gradient | 0.409 | 16.63 | 0.432 | 0.85 | ✓ Certified | Barron et al., CVPR 2022 |
Dataset
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%