Continuous and automatic registration of live RGBD video streams with partial overlapping views
Bibliographic record
Abstract
This paper presents a novel method for automatic registration of video streams originated from two depth-sensing cameras. The system consists of a sender and receiver, in which the sender obtains the streams from two RGBD sensors placed arbitrarily around a room and produces a unified scene as a registered point cloud. A conventional method to support a multi-depth sensor system is through calibration. However, calibration methods are time consuming and require the use of external markers prior to streaming. If the cameras are moved, calibration has to be repeated. The motivation of this work is to facilitate the use of RGBD sensors for non-expert users, so that cameras need not to be calibrated, and if cameras are moved, the system will automatically recover the alignment of the video streams. DeReEs [Seifi et al. 2014], a new registration algorithm, is used, since it is fast and successful in registering scenes with small overlapping sections.
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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".