Camera Self-Calibration and Three Dimensional Reconstruction under Quasi-Perspective Projection
Bibliographic record
Abstract
The problem of camera self-calibration and Euclidean reconstruction from image sequences is addressed in the paper. We propose a quasi-perspective projection model and apply the model to structure and motion factorization to estimate the focal lengths of the cameras. Then we optimize the camera parameters based on Kruppa constraints and recover the metric structure from factorization of the normalized tracking matrix. The novelty and contribution of the paper lies in two aspects. First, under the assumption that the camera is far away from the object with small rotations, we propose that the imaging process can be modeled by quasi-perspective projection. The model is more accurate than affine camera model since the projective depths are implicitly embedded. Second, we propose to calibrate a more general camera model with 5 intrinsic parameters, while previous factorization algorithm can only calibrate the focal lengths. We validate and evaluate the proposed method on many synthetic and real image sequences and show the improvements over existing solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".