Bibliographic record
Abstract
Collecting dense range measurements in uncontrolled environments is a challenging problem as lighting and surfaces' texture significantly influence the quality of the measurements. Instead of concentrating on improving a specific type of range sensors, the overall quality of the sensing can also be enhanced through the development of a mechanism that combines various range sensing technologies to form a multi-modal range sensor. Although many different multi-modal systems have been investigated, the problem of merging datasets have hinder engineers from producing unified data. Two major approaches have been used to rectify this problem: system calibration of the multi-modal system and data fitting of all datasets into a single model, which the latter is more widely used. The lack of multi-modal system calibration approaches is due to their complicated and lengthy nature, where individual calibration approaches must be applied to each subsystem and then applied between subsystems of the multi-modal range sensor. To alleviate the problems in multi-modal system calibration, straightforward and generic guidelines for calibration are defined and applied to an in-house multi-modal system built from a laser range finder system, two active triangulation systems using structured lighting, and a stereovision system. This paper addresses the system's intra- and inter-calibration processes and presents renderings of datasets collected with the calibrated multi-modal range sensor without the use of data fitting. From these results, the potential benefits of multi-modal calibration that reduces the need of data fitting and the advantages of merging subsystem's strengths to complement other subsystem's weaknesses are put in evidence
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".