Towards Global Localization Using Global Descriptors
Bibliographic record
Abstract
3D pose of a camera with respect to a 3D representation of thescene. IBL, despite being a trivial problem for small scenes, becomesquite challenging as the size of the scene grows. Aside fromthe computational burden, matching against a very large numberof 3D keypoints spanning a wide variety of viewpoints, illumination,and areas is a very unreliable process that results in a largenumber of outliers and ambiguous situations. In recent years, anumber of approaches have attempted to address the problem usingparadigms such as bag-of-words, features co-occurrence andothers, with varying degrees of success. This paper explores theuse of global descriptors, in particular GIST, to tackle this problem.We present a system that relies on a similarity measure derivedfrom GIST to qualify a limited number of 3D points for the matchingprocess, hence reducing the problem to its small size counterpart.Our results on a standard dataset show that our system canachieve better localization accuracy than the state of the art at afraction of the computational cost, which can used towards globallocalization.
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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".