Evaluating the Performance of Close-Range 3D Active Vision Systems for Industrial Design Applications
Bibliographic record
Abstract
In recent years, active three-dimensional (3D) active vision systems or range cameras for short have come out of research laboratories to find niche markets in application fields as diverse as industrial design, automotive manufacturing, geomatics, space exploration and cultural heritage to name a few. Many publications address different issues link to 3D sensing and processing but currently these technologies pose a number of challenges to many recent users, i.e., "what are they, how good are they and how do they compare?". The need to understand, test and integrate those range cameras with other technologies, e.g. photogrammetry, CAD, etc. is driven by the quest for optimal resolution, accuracy, speed and cost. Before investing, users want to be certain that a given range camera satisfy their operational requirements. The understanding of the basic theory and best practices associated with those cameras are in fact fundamental to fulfilling the requirements listed above in an optimal way. This paper addresses the evaluation of active 3D range cameras as part of a study to better understand and select one or a number of them to fulfill the needs of industrial design applications. In particular, object material and surface features effect, calibration and performance evaluation are discussed. Results are given for six different range cameras for close range applications.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".