Datasets › Middlebury 2014
Middlebury 2014
The Middlebury 2014 dataset contains a set of 23 high resolution stereo pairs for which known camera calibration parameters and ground truth disparity maps obtained with a structured light scanner are available. The images in the Middlebury dataset all show static indoor scenes with varying difficulties including repetitive structures, occlusions, wiry objects as well as untextured areas.
Source: Using Self-Contradiction to Learn Confidence Measures in Stereo Vision Image Source: https://vision.middlebury.edu/stereo/data/scenes2014/
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Monocular Depth Estimation | Middlebury 2014 | Miangoleh et al. (MiDaS) ORD 0.3467 | Boosting Monocular Depth Estimation Models to... | compphoto/BoostingMonocularDepth | 2 | Compare |
| Stereo Disparity Estimation | Middlebury 2014 | MoCha-V2 D1 Error (2px) 3.51 | MoCha-Stereo: Motif Channel Attention Network for Stereo Matching | zyangchen/mocha-stereo | 2 | Compare |
Papers archive 2025-07-28
3 shown of 3 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 59. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| MoCha-Stereo: Motif Channel Attention Network for Stereo Matching | 1 | 1 | 10 Apr 2024 | ran 16 of 17 samples (1 unverified) |
| RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching | 1 | 1 | 15 Sep 2021 | ran 1 of 2 samples (1 unverified; 1 pointer-only for licence) |
| Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging | 1 | 2 | 28 May 2021 | ran 2 of 2 samples (0 unverified; 2 pointer-only for licence) |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Unknown
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- Middlebury 2014
1 variant name, as the archive lists them.
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