3D Reconstruction of Exposed Underground Utilities Using Photogrammetric Methods
Bibliographic record
Abstract
This thesis addresses the topic of three-dimensional (3D) reconstruction of exposed underground utilities using photogrammetric methods. Research on this topic is mainly motivated by the need for improved information on the location of underground utilities and, thus, to provide reliable information for the management of buried assets. In this thesis, a system of photogrammetric software programs is developed for 3D reconstruction of underground utilities. Camera calibration programs are used for computing interior elements and lens distortion coefficients of digital cameras and saving them in a lookup table (LUT). The accuracy of calibrated image coordinates satisfies the photogrammetric processing demand. An automatic image point detection method is proposed and achieved in these programs. External orientation programs are used for calculating exterior elements of the digital images. Based on geographic information system (GIS) and global positioning system (GPS) techniques, a new ground control points (GCPs) collection method is proposed and implemented in these programs. A 3D reconstruction program provides corresponding functions to obtain and edit 3D information of underground utilities. Epipolar lines are employed as an assisting tool that helps operators easily find homologous points from different digital images. The study results indicate that photogrammetric methods for reconstructing 3D information of underground utilities are effective and low cost.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".