A high resolution 3D laser camera for 3D object digitization
Bibliographic record
Abstract
With the advance of linear CCD arrays and high precision galvanometer design in recent years, triangulation based 3D laser cameras have found wide applications from human contour digitization to object tracking and imaging on the International Space Station. [1] In most applications, a beam size of 1mm or larger is used to minimize the beam divergence over the entire range. With a beam diameter of 1mm, the position resolution (X, Y direction) is normally in the order of one millimeter. In the triangulation method, the distance (Z direction) information is extracted from the position of a Gaussian shape peak on a detector array. There are two major sources of error, excessive edge effects and speckle noise caused by a large spot size. Edge effects are produced when parts of the same beam spot fall on surfaces at different distances. This causes the peak shape of the imaging spot on the array to deviate from Gaussian and produces errors in the distance measurement at the edge of an object. In this paper, modeling of edge effects and speckle noise in an auto-synchronized 3D laser camera in terms of beam size, laser wavelength, optical aperture and geometrical parameters used in the triangulation arrangement are discussed. The methods to mitigate errors from edge effects and speckle noise, and the results showing high resolution in both lateral position and distance on a 3D object are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".