Public Building Geometric Models From Point Clouds: A multidimensional quality evaluation framework
Bibliographic record
Abstract
With the rapid development of the low-altitude economy, the application of 3D building geometric models has become increasingly critical in fields such as urban planning and management, disaster emergency response, virtual reality, augmented reality, and digital twins. Due to the advancements in fundamental surveying and mapping technologies as well as computer vision, datasets of building geometric models based on ubiquitous point clouds have continuously emerged. However, the created models often suffer from low lightweight properties, strong dependence on prior knowledge of building structures, topological inconsistency, and insufficient or even absent semantic representation. These problems have resulted in building model datasets exhibiting significant disparities in geometric accuracy, topological structure, and semantic richness, alongside a lack of unified quality assessment standards. To address this, this article proposes a multidimensional quality evaluation framework for building geometric models, encompassing aspects such as geometric accuracy, topological correctness, semantic richness, lightweight properties, and model modality. This framework is employed to comprehensively evaluate six representative building model datasets. By examining common issues in existing datasets, such as geometric distortions, topological errors, and semantic deficiencies, a series of optimization strategies and solutions are proposed. Considering diverse application requirements, this article emphasizes balance among geometric accuracy, topological relationships, semantic richness, and lightweight to meet the demands of multiscenario applications. Furthermore, the article explores future directions for the construction of building model datasets, recommending a focus on multilevel detail representation, uncertainty assessment of quality, and alignment with practical application demands. These efforts aim to drive the optimization and intelligent development of 3D building models, providing higherquality support for applications such as digital twins and the low-altitude economy.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".