Workshop Report 08w5070 Multi-View and Geometry Processing for 3D Cinematography
Bibliographic record
Abstract
By 3D cinematography we refer to techniques to generate 3D models of dynamic scenes from multiple cameras at video frame rates. Recent developments in computer vision and computer graphics, especially in such areas as multiple-view geometry and image-based rendering have made 3D cinematography possible. Important applications areas include production of stereoscopic movies, full 3D animation from multiple videos, special effects for more traditional movies, and broadcasting of multiple-viewpoint television, among others. The aim of this workshop was to bring together scientists and practitioner who have contributed to the mathematical foundations of the field, as well as those who have developed working systems. There were 20 participants from Canada, the United States, Europe and Asia. A total of 20 talks of length 30 minutes were presented during the five-day workshop. A book comprising extended versions of these presentations is currently under production, and will be published by Springer-Verlag in 2010 [3]. 1 Overview of the Field The name 3D cinematography is motivated by the fact that it extends traditional cinematography from 2D (images) to 3D (solid objects that we can render with photorealistic textures from arbitrary viewpoints) at the same frame rate. A first workshop on 3D Cinematography took place in New York City in June 2006
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.296 | 0.213 |
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".