Motion Occlusions for Automatic Generation of Relative Depth Maps
Bibliographic record
Abstract
Recovering of the depth structure of a scene from monocular video content provides an important advantage in applications such as AR (placing and removing of objects) or 3D-TV and 3D cinema (2D-to-3D video conversion). In this paper, we present an automatic method to generate relative depth maps from monocular video sequences. It relies on the dynamic occlusion depth cue to recover the depth order of objects in the scene. The forward and backward motion analysis between each two consecutive frames allows the calculation of their dynamic occlusions. We estimate the motion using a modified version of the EpicFlow. Our modifications to this optical flow method made it coherent in forward-backward directions without compromising its performance. Thanks to this new feature, occlusions are simpler to calculate than the approaches used in the relevant literature. The obtained occlusions allow order deduction of the objects contained in the image. These objects are obtained using a segmentation approach which considers both color and motion. Ours results show a small improvement to the quality of the optical flow while adding the forward/backward coherence. With respect to the depth ordering our approach obtains slightly better results than the reference method while removing a computationally costly step from the processing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".