Self-Supervised Pre-Training for 3-D Roof Reconstruction on LiDAR Data
Bibliographic record
Abstract
Reconstructing building roofs from light detection and ranging (LiDAR) point clouds from aerial perspectives is significantly important in photogrammetry domains. This letter proposes a novel approach for three-dimensional (3D) real-world building roof reconstruction in Estonia, employing a two-stage self-supervised pre-training architecture to transform 3D roof point clouds into wireframe models. We utilize a self-supervised pre-training framework that incorporates a purpose-designed and efficient self-attention mechanism to generate point-wise features. Subsequently, we develop modules for corner detection and edge prediction to classify and regress the coordinates of corner points and determine optimal edge selections, respectively, to construct the final wireframe model. The effectiveness of our approach is evaluated on real-world roof datasets, achieving corner and edge precision accuracies of 83% and 78%, respectively. In addition, fine-tuning our self-supervised pre-training method with varying ratios of labeled data, particularly with only 50% partially labeled data, attains superior performance, achieving 84% and 85% corner and edge precision, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".