Using Projective vision to Find Camera Positions in an Image Sequence
Bibliographic record
Abstract
The paradigm of projective vision has recently become popular. In this paper we describe a system for computing camerapositions from an image sequence using projective methods. Projective methods are normally used to deal with uncalibrated images. However, we claim that even when calibration information is available it is often better to use the projective approach. By computing the trilinear tensor it is possible to produce a reliable and accurate set of correspondences. When calibration information is available these correspondences can be sent directly to a photogrammetric program to produce a set of camera positions. We show one way of dealing with the problem of cumulative error in the tensor computation and demonstrate that projective methods can handle surprisingly large baselines, in certain cases one third of the image size. In practice projective methods, along with random sampling algorithms, solve the correspondence problem for many image sequences. To aid in the understanding of this relatively new paradigm we make our binaries available for others on the web. Our software is structured in a way that makes experimentation easy and includes a viewer for displaying the final results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".