Enhancement and assessment of large vision models for 3D particle reconstruction from X-ray tomography
Bibliographic record
Abstract
Three-dimensional (3D) particle reconstruction from X-ray micro-computed tomography (µCT) images is essential for digital twins and understanding the micromechanical behaviors of granular media. Despite large vision models (LVMs) having shown remarkable effectiveness across various domains, their application to accurate 3D particle reconstruction remains underexplored. This study proposes a systematic framework that enhances and leverages LVMs for the reconstruction of arbitrary 3D particles. The proposed framework includes three key steps: (1) enhancing LVMs with higher computational efficiency for two-dimensional (2D) label extraction, (2) mapping stacked 2D labels to 3D, and (3) extracting particle surfaces to generate 3D models. The enhanced approach is applied to reconstruct four distinct samples to validate feasibility. Six conventional and four lightweight LVMs are selected to explore the influence of model size and the number of prompts on reconstruction accuracy. The H-extreme watershed method is chosen as a benchmark for comparison. The results demonstrate that the enhanced framework can accurately reconstruct irregular and complex samples, such as carbonate sands, with a greater than 50% improvement in accuracy compared to the benchmark prediction. Additionally, the framework effectively reduces over- and under-segmentation errors, resulting in accurate reconstruction of microstructural characteristics. This versatile framework presents a promising alternative for investigating complex micromechanical mechanisms of granular media.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".