Bibliographic record
Abstract
Time-of-flight-based three-dimensional cameras are the state-of-the-art imaging modality for acquiring rapid 3D position information. Unlike any other technology on the market, it can deliver 2D images co-located with distance information at every pixel location, without any shadows. Recent technological advancements have begun miniaturizing such technology to be more suitable for laptops and eventually cellphones. This paper explores the systematic errors inherent to the new PMD CamBoard nano camera. As the world’s most compact 3D time-of-flight camera it has applications in a wide domain, such as gesture control and facial recognition. To model the systematic errors, a one-step point-based and plane-based bundle adjustment method is used. It simultaneously estimates all systematic errors and unknown parameters by minimizing the residuals of image measurements, distance measurements, and amplitude measurements in a least-squares sense. The presented self-calibration method only requires a standard checkerboard target on a flat plane, making it a suitable candidate for on-site calibration. In addition, because distances are only constrained to lie on a plane, the raw pixel-by-pixel distance observations can be used. This makes it possible to increase the number of distance observations in the adjustment with ease. The results from this paper indicate that amplitude dependent range errors are the dominant error source for the nano under low scattering imaging configurations. Post user self-calibration, the RMSE of the range observations reduced by almost 50%, delivering range measurements at a precision of approximately 2.5cm within a 70cm interval.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".