A General Framework for Fast 3D Object Detection and Localization Using an Uncalibrated Camera
Bibliographic record
Abstract
In this paper, we present a real-time approach for 3D object detection using a single, mobile and uncalibrated camera. We develop our algorithm using a feature-based method based on two novel naive Bayes classifiers for viewpoint and feature matching. Our algorithm exploits the specific structure of various binary descriptors in order to boost feature matching by conserving descriptor properties (e.g., rotational and scale invariance, robustness to illumination variations and real-time performance). Unlike state-of-the-art methods, our novel naive classifiers only require a database with a small memory footprint because we store efficiently encoded features. In addition, we also improve the indexing scheme to speed up the matching process. Because our database is built from powerful descriptors, only a few images need to be 'learned' and constructing a database for a new object is highly efficient.
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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 | 0.000 |
| 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".