A fast modeling method for augmented reality dynamic scenes with spatio-temporal semantic constraints
Bibliographic record
Abstract
Augmented reality (AR) scene modeling with virtual-real integration is an effective way to enhance users’ perception and understanding of geographic spaces. However, the existing modeling methods focus on precise virtual-real alignment in static scenes using single-frame images, leading to inefficiencies in dynamic scene modeling and low accuracy in virtual-real integration. This paper proposes a fast modeling method for AR dynamic scenes with spatio-temporal semantic constraints. By thoroughly analyzing spatio-temporal semantic constraint rules in AR dynamic scene modeling, a keyframe extraction algorithm based on a synchronized spatio-temporal semantic distance measurement model was designed. A rapid spatio-temporal interpolation model for AR dynamic view poses with spatio-temporal semantic association was established, and a real 3D scene-driven fast twin modeling method for AR dynamic scenes was proposed. Experimental results show that the proposed method reduces redundant image matching computations by 87.53% while maintaining virtual-real registration accuracy above 1°. This method enables accurate sampling of keyframes with spatio-temporal homogeneity, avoids redundant transmission of large volumes of frame image data, and improves AR dynamic scene virtual-real registration efficiency while maintaining accuracy. Furthermore, the spatial semantic information in real 3D scenes effectively guides fast AR dynamic scene modeling.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".