Online Training of Stereo Self-Calibration Using Monocular Depth Estimation
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
Stereo imaging is the most common passive method for producing reliable depth maps. Calibration is a crucial step for every stereo-based system, and despite all the advancements in the field, most calibrations are still done by the same tedious method using a checkerboard target. Monocular-based depth estimation methods do not require extrinsic calibration but generally achieve inferior depth accuracy. In this paper, we present a novel online self-calibration method, which makes use of both stereo and monocular depth maps to find the transformation required for extrinsic calibration by enforcing consistency between both maps. The proposed method works in a closed-loop and exploits the pre-trained networks' global context, and thus avoids feature matching and outliers issues. In addition to presenting our method using an image-based monocular depth estimation method, which can be implemented in most systems without additional changes, we also show that adding a phase-coded aperture mask leads to even better and faster convergence. We demonstrate our method on road scenes from the KITTI vision benchmark and real-world scenes using our prototype camera. Our code is publicly available at https://github.com/YotYot/CalibrationNet.
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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