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 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 | 0.000 | 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) | 0.001 | 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 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".