Dev
Machine Vision / AOI — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| focus_distance_error | -1.2 – 1.8 | mm |
| lens_distortion_k1 | -0.024 – 0.036 | - |
| exposure_time_drift | 9.52 – 10.72 | ms |
| white_balance_gain | 0.976 – 1.036 | - |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | UniAD + gradient | 0.742 | 30.05 | 0.918 | 0.87 | ✓ Certified | You et al., NeurIPS 2022 |
| 2 | Template Match + gradient | 0.631 | 24.82 | 0.796 | 0.82 | ✓ Certified | Brunelli, Template Matching, 2009 |
| 3 | PatchCore + gradient | 0.620 | 23.78 | 0.761 | 0.89 | ✓ Certified | Roth et al., CVPR 2022 |
| 4 | PnP-ADMM + gradient | 0.601 | 23.73 | 0.759 | 0.8 | ✓ Certified | Venkatakrishnan et al., 2013 |
Visible Data Fields
y
H_ideal
spec_ranges
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%