The Concept of Levels of Detail for 3D Niche Models in CityGML
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Buddhist niches in grottoes can be represented in three-dimensional (3D) for their detailed geometries on surfaces by using triangular meshes generated from point clouds. However, not all applications require 3D models with high geometric detail. The mesh models of niches have drawbacks such as large data volumes, lack of semantic information, and absence of spatial relationships between structural components and members within niches. Those limitations make mesh models suitable only for visualization and challenging to use directly in tasks like spatial analysis, simulation experiments, mechanical analysis, and disease investigation. To address this problem, this study defines four Levels of Detail (LoDs) for Buddhist niches, ranging from LoD0 to LoD3, drawing on the concept of LoDs for urban buildings in CityGML 3.0. As the LoD level increases, 3D models contain more detailed geometries, including spatial points, bounding boxes, niche structural components, and component members. Those 3D models at different LoDs can represent niches with varying degrees of abstraction, making them suitable for different applications and guiding the production of standardized 3D semantic models. To validate the feasibility of the LoDs definition for Buddha niches, this paper reconstructs 3D models of niches at different LoDs based on high-precision mesh models. Finally, a comparison is made between the original mesh model and models at different LoDs in terms of data size and potential application scenarios.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it