Automatic alignment and graph map building of panoramas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Panoramic cameras can capture a 360/spl deg/ view from a point providing new capabilities for multimedia, tele-presence and robotic applications. For example, virtual walk-throughs of an environment can be created from a sequence of panoramic images, where perspective views are created according to a user's position and view direction. For this and other applications, the panoramic images need to be aligned to one another and a topological or metric map created. An automatic method to achieve this would remove a lot of tedious preparations for multimedia systems and enable robotic positioning systems. This work presents three methods to address these problems; finding the relative orientation between panoramas, using the essential matrix is created to determine the relative rotation and translation direction, and an image search based algorithm to detect when the camera path crosses over itself for creating a topological map. The SIFT feature detector is used to find correspondences between panoramic images. Experimental results are shown for determining the rotation and cross-overs.
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.
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 it