Surface reconstruction using neural network mapping of range-sensor images to object space
Bibliographic record
Abstract
Range sensors that employ structured-light triangulation techniques often require calibration procedures, based on the system optics and geometry, to relate the captured image data to object coordinates. A Bernstein basis function (BBF) neural network that directly maps measured image coordinates to object coordinates is described in this paper. The proposed technique eliminates the need to explicitly determine the sensor’s optical and geometric parameters by creating a functional map between image-to-object coordinates. The training and test data used to determine the map are obtained by capturing successive images of the points of intersection between a projected light line and horizontal markings on a calibration bar, which is stepped through the object space. The surface coordinates corresponding to the illuminated pixels in the image are determined from the neural network. An experimental study that involves the calibration of a range sensor using a BBF network is presented to demonstrate the effectiveness and accuracy of this approach. The root mean squared errors for the x and y coordinates in the calibrated plane, 0.25 and 0.15 mm, respectively, are quite low and are suitable for many reverse engineering and part inspection applications. Once the network is trained, a hand carved wooden mask of unknown shape is placed in the work envelope and translated perpendicular to the projected light plane. The surface shape of the mask is determined using the trained network.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".