Automatic real-time 2D-to-3D conversion for scenic views
Bibliographic record
Abstract
The generation of three-dimensional (3D) videos from monoscopic two-dimensional (2D) videos has received a lot of attention in the last few years. Current conversion techniques are based on generating an estimated depth map for each frame from different depth cues, and then using Depth Image Based Rendering (DIBR) to synthesize the additional views. Efficient interactive techniques have been developed in which multiple depth factors (monocular depth cues) are utilized to estimate the depth map using machine-learning algorithms. The challenge with such methods is that they cannot be used for real-time conversion. We address this problem by proposing an effective scheme that generates high quality depth maps for scenic views in real-time. In our work, we use three depth cues, haze, vertical edges, and sharpness to estimate a sparse depth map. We then obtain the full depth map from the sparse map using an edge-aware interpolation method. Performance evaluations show that our method outperforms the existing state-of-the-art 2D-to3D conversion methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".