Perceiver – A Five-view Stereo System for High-quality Disparity Generation and its Application in Video Post-Production
Bibliographic record
Abstract
Element extraction from videos has always been a time-consuming process in the entertainment industry.In this research, we explored the possibility of simplifying the video object extraction technique with corresponding depth sequences.Based on postproduction quality requirements, we developed our disparity enhancing system by integrating our two-axis-multi-view-stereo method that perceives an environment from five different perspectives on both x and y axes.Our research results have shown that the disparity quality of our approach is both visually and quantitatively more accurate than the traditional one-stereo-pair method, and its object extraction (i.e., matting) quality is comparable with existing mature matting technique to a certain extent.This research output can be applied in video object cut-out, visual effects composition, video's 2D to 3D conversion, and image post-processing.With further improvement, our system might be applicable in AR, VR, machine vision, and auto-pilot areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.007 | 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".