2D‐to‐3D Video Conversion: Overview and Perspectives
Bibliographic record
Abstract
This chapter focuses on the rapid development of 2D-to-3D video conversion techniques that allow the generation of stereoscopic video from a standard monoscopic video source. It explores the different methods currently used to define the depth structure of the scene for 2D-to-3D conversion of video content. The chapter classifies the methods into two main categories: 1) Depth creation methods; and 2) Depth recovery methods. The 2D-to-3D video conversion is related to the generation of the second video stream for stereoscopic visualization. Ideally, 2D-to-3D conversion should produce content that has no picture artifacts, that has good depth, and that respects the artistic composition of the original video. It is clear that the research on 2D-to- 3D video conversion is progressing at a very rapid pace and with very encouraging results. Such progress is simply the consequence of the increasing attention that this research has been receiving from both industry and academia. Controlled Vocabulary Terms stereo image processing; video signal 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".