Basic theory on surface measurement uncertainty of 3D imaging systems
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
Three-dimensional (3D) imaging systems are now widely available, but standards, best practices and comparative data have started to appear only in the last 10 years or so. The need for standards is mainly driven by users and product developers who are concerned with 1) the applicability of a given system to the task at hand (fit-for-purpose), 2) the ability to fairly compare across instruments, 3) instrument warranty issues, 4) costs savings through 3D imaging. The evaluation and characterization of 3D imaging sensors and algorithms require the definition of metric performance. The performance of a system is usually evaluated using quality parameters such as spatial resolution/uncertainty/accuracy and complexity. These are quality parameters that most people in the field can agree upon. The difficulty arises from defining a common terminology and procedures to quantitatively evaluate them though metrology and standards definitions. This paper reviews the basic principles of 3D imaging systems. Optical triangulation and time delay (timeof- flight) measurement systems were selected to explain the theoretical and experimental strands adopted in this paper. The intrinsic uncertainty of optical distance measurement techniques, the parameterization of a 3D surface and systematic errors are covered. Experimental results on a number of scanners (Surphaser®, HDS6000®, Callidus CPW 8000®, ShapeGrabber® 102) support the theoretical descriptions.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 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