Automatic and adaptable registration of live RGBD video streams
Bibliographic record
Abstract
We introduce DeReEs-4V, an algorithm that receives two separate RGBD video streams and automatically produces a unified scene through RGBD registration in a few seconds. The motivation behind the solution presented here is to allow game players to place the depth-sensing cameras at arbitrary locations to capture any scene where there is some partial overlap between the parts of the scene captured by the sensors. A typical way to combine partially overlapping views from multiple cameras is through visual calibration using external markers within the field of view of both cameras. Calibration can be time consuming and may require fine tuning, interrupting gameplay. If the cameras are even slightly moved or bumped into, the calibration process typically needs to be repeated from scratch. In this article we demonstrate how RGBD registration can be used to automatically find a 3D viewing transformation to match the view of one camera with respect to the other without calibration while the system is running. To validate this approach, a comparison of our method against standard checkerboard target calibration is provided, with a thorough examination of the system performance under different scenarios. The system presented supports any application that might benefit from a wider operational field-of-view video capture. Our results show that the system is robust to camera movements while simultaneously capturing and registering live point clouds from two depth-sensing cameras.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".