SoccerNet-Depth: a Scalable Dataset for Monocular Depth Estimation in Sports Videos
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monocular Depth Estimation (MDE) is fundamental in sports video understanding, enhancing augmented graphics, scene understanding, and game state reconstruction. Despite remarkable progress in autonomous driving and indoor scene understanding, there is currently a lack of MDE datasets tailored for sports. Furthermore, most existing datasets only focus on single images, disregarding the temporal aspect. In this work, we introduce the first video dataset for MDE in sports, SoccerNet-Depth, focusing on football and basketball videos. In particular, we leverage the graphic engine from video games to automatically extract video sequences and their associated depth maps, making our dataset easily scalable. Furthermore, we benchmark and fine-tune several state-of-the-art MDE methods on our dataset. Our analysis shows that MDE in sports is far from being solved, making our dataset a perfect playground for future research. Dataset and codes: https://github.com/SoccerNet/sn-depth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it