WSPointNet: A multi-branch weakly supervised learning network for semantic segmentation of large-scale mobile laser scanning point clouds
Bibliographic record
Abstract
Semantic segmentation of large-scale mobile laser scanning (MLS) point clouds is essential for urban scene understanding. However, most of the existing semantic segmentation methods require a large quantity of labeled data, which are labor-intensive and time-consuming. To this end, we propose a multi-branch weakly supervised learning network (WSPointNet) to solve this challenge. Our method includes a basic weakly supervised framework and a multi-branch weakly supervised module. With input point clouds and few labels, the basic weakly supervised framework outputs the prediction values of the input point clouds and the underlying supervised signals of the whole network. Next, the multi-branch weakly supervised module explores the potential information of the unlabeled and labeled points while preventing model over-fitting. Concretely, the module includes an ensemble prediction constraint branch, a contrast-guided entropy regularization branch, and an adaptive pseudo-label learning branch. The ensemble prediction constraint branch aims to enhance the prediction stability of the point cloud. The contrast-guided entropy regularization branch is proposed to prevent model over-fitting by comparing the ensemble prediction labels with the current prediction labels. The adaptive pseudo-label learning branch provides efficient and adaptive supervised signals for model training by the consistency cost and ensemble prediction. Extensive experiments conducted on two MLS benchmarks showed that our WSPointNet achieved a promising semantic segmentation performance with sparse annotated points. For the public Toronto3D dataset, with only 0.1% labeled points, our WSPointNet obtained an overall accuracy of 96.76% and a mIoU of 78.96%, which outperformed most of comparative fully supervised 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 | 0.001 | 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 | 0.000 |
| 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".