2DTriPnP: A Robust Two-Dimensional Method for Fine Visual Localization Using Google Streetview Database
Bibliographic record
Abstract
The complete camera pose (location + orientation) of the Google street view (GSV) images is provided by Google. Hence, one can utilize this information to localize a query camera based on the projective geometry. The existing literature works either perform image retrieval-based rough location recognition or require high-computational power/specific features for three-dimensional fine localization. In this paper, we propose a robust 2-D method for outdoor image-based localization using the GSV database. Having found the nearest neighboring images (best matches) in the GSV database using image retrieval techniques or the GPS circle information, the proposed method can be applied for robust fine localization of pedestrians/vehicles. The proposed method first finds the common features among the three views, i.e., query view and two from the best matches. Next, for each common feature, a 2-D triangulation is performed using the retrieved database images to find the feature world coordinates. We call this procedure “2DTri.” Afterward, a novel set of nonlinear equations is solved to estimate the fine location of the query. The novel set of equations can be interpreted as a 2-D version of the well-known perspective n-point (PnP) problem, which we call “2DPnP.” Hence, the proposed method is named “2DTriPnP.” The 2DPnP step is performed in a robust way, which is more accurate and considerably less complex compared to the conventional RANSAC-based robust methods. 2DTriPnP will be demonstrated experimentally to show better localization performance compared to other state-of-the-art methods.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".