Semi-automatic 3D Reconstruction of Occluded and Unmarked Surfaces from Widely Separated Views
Bibliographic record
Abstract
Three-dimensional modeling from images, when carried out entirely by a human, is time consuming and impractical for large-scale projects. On the other hand, full automation may still be unachievable for many applications. In addition, 3D modeling from images requires the extraction of features and needs them to appear in multiple images. However, in practical situations those features are not always available, sometimes not even in a single image, due to occlusions or lack of texture. Taking closely separated images or optimally designing view locations can preclude some occlusions. However, taking such images is often not practical and we are usually left with images that do not properly cover every detail. This paper argues that widely separated views and a semi-automated technique are the logical solutions to 3D construction of large and complex objects or environments. The proposed approach uses both interactive and automatic techniques, each where it is best suited, to accurately and completely model man-made structures and objects. It particularly focuses on automating the construction of unmarked surfaces such as columns, arches, steps and blocks from minimum seed points. It also extracts the occluded or invisible corners and lines from existing ones. Many examples, such as Arc de Triomphe in Paris and Florence's St. John baptistery, are completely modeled from a small number of images taken by tourists.
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".