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
We present DeReEs, a real-time RGBD registration algorithm for the scenario where multiple RGBD images of the same scene are obtained from depth-sensing cameras placed at different viewpoints, with partial overlaps between their views. DeReEs (Detection, Rejection and Estimation) is a combination of 2D image-based feature detection algorithms, a RANSAC based false correspondence rejection and a rigid 3D transformation estimation. DeReEs performs global registration not only in real-time, but also supports large transformation distances for both translations and rotations. DeReEs is designed as part of a virtual/augmented reality solution for a remote 3D collaboration system that does not require initial setup and allows users to freely move the cameras during use. We present comparisons of DeReEs with other common registration algorithms. Our results suggest that DeReEs provides better speed and accuracy especially in scenes with partial overlapping.
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