Bibliographic record
Abstract
Creating geometrically correct and complete 3D models of complex environments remains a difficult problem. Techniques for 3D digitizing and modeling have been rapidly advancing over the past few years although most focus on single objects or specific applications such as architecture and city mapping. The ability to capture details and the degree of automation vary widely from one approach to another. One can safely say that there is no single approach that works for all types of environment and at the same time is fully automated and satisfies the requirements of every application. In this paper we show that for complex environments, those composed of several objects with various characteristics, it is essential to combine data from different sensors and information from different sources. Our approach combines models created from multiple images, single images, and range sensors. It can also use known shapes, CAD, existing maps, survey data, and GPS. 3D points in the image-based models are generated by photogrammetric bundle adjustment with or without self-calibration depending on the image and point configuration. Both automatic and interactive procedures are used depending on the availability of reliable automated process. Producing high quality and accurate models, rather than full automation, is the goal. Case studies in diverse environments are used to demonstrate that all the aforementioned features are needed for environments with a significant amount of complexity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".