Self-Supervised Pretraining Framework for Extracting Global Structures From Building Point Clouds via Completion
Bibliographic record
Abstract
The exterior structural information of buildings are crucial for advancing smart city initiatives and reconstructing 3-D edifices. However, practical obstacles, such as sparse or incomplete building point clouds—stemming from various scanning angles or sensor limitations—present significant challenges. To mitigate the high costs and labor demands associated with data labeling, we introduce an innovative pretraining framework with self-supervised learning (SSL) that incorporates a modified point cloud completion (PCC) subnetwork to extract building structures. Specifically, the modified PCC subnetwork completes the original partial building point clouds by capturing both fine-grained and high-level semantic information of 3-D shapes. Following this, self-supervised feature extractor using a masked autoencoder (MAE) and a multiscale feature mechanism generates pointwise features from the completed building point clouds. We evaluate the effectiveness of the proposed integrated framework in extracting global structures using both established wireframe construction methods and our newly proposed edge point identification that incorporates a novel edge point regression loss. Extensive experimental results demonstrate that our modified PCC network reaches a 93.5% convergence rate that is higher than the results from competing methods. Our self-supervised pretraining framework extracts more accurate global structures with better loss convergence than traditional edge point identification loss designs. Finally, our combined framework improves performance for subsequent processes (such as wireframe construction and edge point identification) when using completed datasets instead of the original partial datasets.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".