The role of feature visibility constraints in perspective alignment
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Bibliographic record
Abstract
Perspective alignment is a novel method of solving backprojection, the well-known problem of computing three dimensional (3D) position and orientation (pose) of a model from two-dimensional (2D) image features. This paper demonstrates that previous backprojection methods can violate the visibility constraint by computing solution poses in which the model occludes features which should be visible. By definition, these visibility errors are associated with incorrect pose solutions. Yet they occur frequently when previous backprojection methods are used in underconstrained situations. We empirically analyze the frequency and consequences of visibility errors in previous backprojection methods. We then show how perspective alignment satisfies the visibility constraint during the pose solution process to eliminate these errors. The algorithm has been implemented and used in a real-time model-based object tracking system. We describe the algorithm and results of tracking real objects in real-time. The algorithm also has implications for reducing the combinatorics of image-model feature pairing in model-based recognition.
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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