Points2Model: a neural-guided 3D building wireframe reconstruction from airborne LiDAR point clouds
Bibliographic record
Abstract
3D building wireframe models offer a simple, flexible, yet effective means of digitally representing real-world buildings with numerous application benefits. However, generating them from airborne LiDAR point clouds (APCs) is challenging due to issues like façade/roof occlusions, point density variations and noise. To create accurate building wireframe models effectively in the face of these issues, we propose explicitly learning to fill in the areas of occlusion in the APC and implicitly learning to enhance the point resolution via up-sampling for effective primitive extraction. To generate wireframe models from the up-sampled points, we developed a corner-edge hypothesis and selection strategy, where optimal corner and edge candidates and their accurate assembly are determined via a set of constraints. Experiments conducted on data from the Building3D dataset demonstrate that our proposed pipeline can effectively reconstruct wireframe models from APCs despite its challenges. Ablations and comparison with other existing methods further show the need for point completion and up-sampling processes in surface reconstruction pipelines.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".