Shape-preserving mesh decimation for 3D building modeling
Bibliographic record
Abstract
We propose a shape-preserving building model reconstruction method that involves simplifying the original building mesh to accommodate various building shapes and complexities. To achieve this, we apply a structure-aware segmentation technique to parse the ubiquitous building points into building geometric primitives and building structural points, i.e., anchor points. After that, we generate dense building meshes from building semantic points in a topology-aware manner. As the geometric primitive semantics are assigned to the building points during the structure-aware segmentation process, these primitive semantics of the building points can be explicitly transferred into the created building meshes. To offer lightweight and accurate building models with enriched semantics, we leverage the building structural points as constraints for the subsequent edge collapse simplification algorithm. This algorithm effectively decimates irrelevant vertices and meshes, while preserving the essential building structural contours. The entire simplification process is performed in a shape-preserving manner, granting us flexible control over the imposition of different degrees of strength regarding various geometric primitives during the simplification. We conduct qualitative and quantitative analyses to evaluate the effectiveness of our method on both individual building models and a large-scale urban scene. Additionally, we extensively compare our proposed shape-preserving algorithm with other state-of-the-art mesh decimation methods to demonstrate our superiority.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".