Façade Separation in Ground-Based LiDAR Point Clouds Based on Edges and Windows
Bibliographic record
Abstract
Extraction of individual façades from building point clouds collected by ground-based LiDAR is vital for urban mapping and modeling. It is a challenging task due to the complexity of façades, and very few studies have been conducted. In this paper, we present a new façade separation method that can divide connected façades into building instances using point coordinates only. The proposed method consists of two steps. The first step is extracting edges and windows from building point clouds. In this step, an improved window detection method which can detect complex windows, such as bay windows, is proposed. In the second step, the separation problem is solved by minimizing a new objective function, which considers both the edge intersections and wall elevations. This objective function is constrained by the number and positions of windows. After the optimization, a subset of individual façades defined by dividing lines will be selected from potential façade candidates. The proposed method is tested in two ground-based LiDAR datasets, which contain façades of various building architectures. One dataset is acquired by static terrestrial laser scanning, and mobile LiDAR collects the other dataset. The overall precision and recall of our façade separation results are 85.5% and 74.6%, respectively. We also compare our methods with existing window detection approaches and other possible façade separation algorithms. These experiments demonstrate the effectiveness and advantages of the proposed methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".