Generic design methodology for the development of three-dimensional structured-light sensory systems for measuring complex objects
Bibliographic record
Abstract
Structured-light (SL) techniques are emerging as popular noncontact approaches for obtaining three-dimensional (3-D) measurements of complex objects for real-time applications in manufacturing, bioengineering, and robotics. The performance of SL systems is determined by the emitting (i.e., projector) and capturing (i.e., camera) hardware components and the triangulation configuration between them and an object of interest. A generic design methodology is presented to determine optimal triangulation configurations for SL systems. These optimal configurations are determined with respect to a set of performance metrics: (1) minimizing the 3-D reconstruction errors, (2) maximizing the pixel-to-pixel correspondence between the projector and camera, and (3) maximizing the dispersion of the measured 3-D points within a measurement volume, while satisfying design constraints based on hardware and user-defined specifications. The proposed methodology utilizes a 3-D geometric triangulation model based on ray-tracing geometry and pin-hole models for the projector and camera. Using the methodology, a set of optimal system configurations can be determined for a given set of hardware components. The design methodology was applied to a real-time SL system for surface profiling of complex objects. Experiments were conducted with an optimal sensor configuration and its performance verified with respect to a nonoptimal hardware configuration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".