Line cue augmented perspective structure from motion
Bibliographic record
Abstract
Object shape and camera motion can be recovered from a sequence of images using a set of feature point correspondences. This is known as the structure from motion problem. This paper describes a method of employing geometrical features available in a scene, in the form of straight lines, in a factorization-based structure from motion application. The effects of inaccuracies of feature data can be reduced by constraining the reconstructed features corresponding to the points forming straight lines. Our main contribution in this paper is the use of such geometric features to refine the shape recovery using the current advancements in the factorization method. Reconstructed features are mapped to straight lines and the measurement matrix containing image feature data is updated with the adjusted data. This increases the accuracy of reconstruction perceptually as well as quantitatively. The algorithm consists of first obtaining the reconstruction using singular value decomposition. Mapping 3D lines to sets of feature points is then carried out. The measurement matrix is refined followed by a second phase of factorization and, optionally, normalization to obtain metric reconstruction. Results pertaining to both synthetic and actual sequences of images are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".