Low-complexity automated depth-order estimation for 2D-to-3D video conversion
Bibliographic record
Abstract
The increasing popularity of 3D TV creates the desire for more 3D video content. Unfortunately, it will take much time for there to be an abundance of 3D video content derived from stereoscopic cameras. However, there currently exists a vast quantity of 2D video material that can potentially be converted to 3D. Converting 2D into 3D is a complex process, and so can be costly. Thus, an automated solution that can be achieved with low-complexity would be desirable. Our past research work has already resulted in a real-time 2D-to-3D conversion technique, but this generates a surrogate depth map that results in pseudo-3D and not necessarily accurate 3D. Our current research focuses on improving the accuracy of the 3D effect by implementing a technique composed of a multi-step process to determine the depth-order of objects, with respect to the camera, in each frame of a video sequence, and incorporating into our existing technique. The multi-step process can be summarized as follows: detect pixels that belong to an edge; use block-based motion estimation to determine if an edge pixel is moving and thus belongs to a moving edge (i.e., occlusion boundary); determine which of either the left or right side block moves with the moving edge pixel, and by deduction determines the occluding object; select seed points from the moving edge pixels; implement color-only region growing from each seed; cluster regions into objects based on their proximity; globally assign depth-order to the objects based on perceived viewing perspective of a frame; and modify the original surrogate depth map to create a more accurate depth map. Test results show that this is a very effective and fast technique for deriving the depth-order of objects and generating more accurate depth map values.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".