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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".