Drone-based mixed reality: enhancing visualization for large-scale outdoor simulations with dynamic viewpoint adaptation using vision-based pose estimation methods
Bibliographic record
Abstract
In recent years, there has been a growing interest in the integration of drones across diverse sectors, particularly within architecture, engineering, and construction (AEC). The amalgamation of drones with mixed reality (MR) stands out as a promising avenue. Proposed applications include the comparison of design and actual objects, as well as landscape simulation in urban design. A previous study successfully developed a drone-viewpoint MR system with low model dependence, leveraging general drones and methods. However, the alignment between the real and virtual worlds was contingent on predefined flight routes, limiting adaptability during MR execution. This study introduces a new model-independent drone viewpoint MR system that integrates two vision-based attitude estimation methods, enabling execution on arbitrary flight paths. Evaluation of the prototype for system latency and alignment accuracy revealed an overall latency of 3.5 s. The alignment accuracy, assessed using intersection over union, demonstrated performance equal to or surpassing the previous system. While this paper does not showcase MR content for practical use, the research lays the groundwork for advancing drone applications in the AEC field. The proposed system offers versatility for MR applications across various stages, from design to maintenance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".