3-D Model-Based Multi-Camera Deployment: A Recursive Convex Optimization Approach
Bibliographic record
Abstract
Based on a convex optimization approach, we propose a new method of multi-camera deployment for visual coverage of a 3-D object surface. In particular, the optimal placement of a single camera is first formulated as translation and rotation convex optimization problems, respectively, over a set of covered triangle pieces on the target object. The convex optimization is recursively applied to expand the covered area of the single camera, with the initially covered triangle pieces being chosen along the object boundary for the first trial through a selection criterion. Then, the same optimization procedures are applied to place the next camera and thereafter. It is pointed out that our optimization approach guarantees that each camera is placed at the optimal pose in some sense for a group of triangles instead of a single piece. This feature, together with the selection criterion for initially covered triangles, reduces the number of operating cameras while still satisfying various constraint requirements such as resolution, field of view, blur, and occlusion. Both simulation and experimental results are presented to show superior performance of the proposed approach, comparing with the results from other existing 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".