Mobile LiDAR Data and Imagery for Digital Twin Generation
Bibliographic record
Abstract
Abstract. Low-cost sensor solutions such as smartphones provide a great opportunity for democratization of mapping among different communities including those working in digital twin application areas. Smartphone acquired imagery and/or built-in LiDAR sensors provide relatively dense point clouds with limited accuracy especially in the absence of GNSS. This type of scanning tool can provide linear measurements in an inexpensive way and can be used with minimal operator training. In this study, we provide two solutions for improving the accuracy of the final point clouds produced by iPhone-based LiDAR and images. One solution utilizes length observations as constraints in the network. The other solution incorporates loosely coupled perspective centre (PC) positions obtained by ultrasonic ranging into a photogrammetric bundle adjustment. The test results show that inclusion of the length observations in the solution improves the relative accuracy of the point cloud for applications such as culvert mapping where absolute accuracy is not of high necessity. In the indoor mapping case, the relative point cloud accuracy for the solutions without and with the PC observations is approximately the same. However, image alignment success and computation time are significantly improved by including the PC observations. Moreover, the inclusion of PC observations provided better compensation of systematic image point errors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".