On the evaluation of photogrammetric methods for dense 3D surface reconstruction in a metrological context
Bibliographic record
Abstract
Abstract. This paper discusses a methodology to evaluate the accuracy of recently developed image-based 3D modelling techniques. So far, the emergence of these novel methods has not been supported by the definition of an internationally recognized standard which is fundamental for user confidence and market growth. In order to provide an element of reflection and solution to the different communities involved in 3D imaging, a promising approach is presented in this paper for the assessment of both metric quality and limitations of an open-source suite of tools (Apero/MicMac), developed for the extraction of dense 3D point clouds from a set of unordered 2D images. The proposed procedural workflow is performed within a metrological context, through inter-comparisons with "reference" data acquired with two hemispherical laser scanners, one total station, and one laser tracker. The methodology is applied to two case studies, designed in order to analyse the software performances in dealing with both outdoor and environmentally controlled conditions, i.e. the main entrance of Cathédrale de la Major (Marseille, France) and a custom-made scene located at National Research Council of Canada 3D imaging Metrology Laboratory (Ottawa). Comparative data and accuracy evidence produced for both tests allow the study of some key factors affecting 3D model accuracy.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".