3-D Model-Based Multi-Camera Deployment: A Recursive Convex Optimization Approach
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it