3D Modeling with Reusable and Integrated Building Blocks
Bibliographic record
Abstract
The creation of highly detailed components presented in this paper builds upon our previously introduced technique for creating complete models from only a small number of measured seed points. Here, we aim at increasing the level of automation in 3D reconstruction of scenes from a small number of images. We build highly detailed model components, for example; windows, columns, groin-vaulted ceilings elements, and arches with large number of automatically created points. The components are created to be reusable in other parts of the model, or any other model, which are similar in shape, but may vary in dimensions. Our approach works directly on the images and performs accurate transformations and scaling automatically without trials and errors. We developed a copy and paste procedure that automatically re-triangulates the base model to account for the pasted element. By creating reusable higher-level building blocks that can be integrated to form a highly detailed model, a growing library of image-based reusable components will materialize. This will significantly reduce the effort, time, and expertise required to create detailed 3D models from images.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".