3-D Reconstruction of Rock Samples via Structure-From-Motion for Virtual Reality Applications: A Methodological Proposal
Bibliographic record
Abstract
This article presents a methodological proposal for the three-dimensional reconstruction of rock samples via structure-from-motion. The presented methodological steps aimed to provide a reproducible workflow to create virtual rock samples to be applied in virtual applications. The proposed methodology works as a how-to guide as well as a preemptive troubleshooting guide for the complete process. Four geologists with different scholar levels volunteered to test this methodological proposal, applying it to three rock samples as the methodology steps were provided in an inverse-proportional manner to the graduate level. When analyzing the results of the performed reconstructions, all analyzed elements presented a proportional reduction due to the lack of information provided. An initial questionnaire was applied to verify the difficulties encountered, and subsequently, all volunteers received the complete methodology. In the second reconstruction, the results were equivalent to those obtained initially with the complete methodology. A technology acceptance model questionnaire was applied to determine the perception of utility and ease of use of the presented methodology. In both cases the results presented themselves in a positive way, indicating that the methodology was able to solve the problems found simply and objectively through a repeatable workflow.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".