The role of feature visibility constraints in perspective alignment
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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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".