Dual Exposure Stereo for Extended Dynamic Range 3D Imaging
Bibliographic record
Abstract
Achieving robust stereo 3D imaging under diverse illumination conditions is challenging due to the limited dynamic range of conventional cameras, causing existing stereo depth estimation methods to suffer from under- or over-exposed images. In this paper, we propose dual-exposure stereo that combines auto-exposure control and dual-exposure bracketing to achieve stereo 3D imaging with extended dynamic range. Specifically, we capture stereo image pairs with alternating dual exposures, which automatically adapt to scene illumination and effectively distribute the scene dynamic range across the dual-exposure frames. We then estimate stereo depth from these dual-exposure stereo images by compensating for motion between consecutive frames. To validate our approach, we develop a robotic vision system, acquire real-world HDR stereo video datasets, and generate additional synthetic datasets. Experimental results demonstrate that our method outperforms existing exposure control methods.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".