Weakly Supervised Large-Scale Point Cloud Semantic Segmentation Based on Dual Consistency Constraints and Uncertainty-Aware Fusion
Bibliographic record
Abstract
Weakly supervised point cloud semantic segmentation has attracted more and more attention due to its capability to circumvent the time-consuming and expensive full labeling annotation process, which is required by fully supervised learning. However, existing weakly supervised methods typically rely solely on the sparsely labeled points for network training and cannot fully exploit the vast amount of unlabeled data. Moreover, recent weakly supervised segmentation methods are not efficient in enhancing network generalization and extracting discriminative features. To resolve these issues, we propose a novel framework (DCUF-Net) for weakly supervised point cloud semantic segmentation based on dual consistency constraints and uncertainty-aware fusion. Specifically, we first design dual perturbations in the data and feature domains to improve the network generalization and employ prediction consistency constraints between the two perturbed branches and the original branch. Additionally, we propose to jointly represent the prediction reliability of unlabeled points according to distribution confidence and uncertainty, which enables us to introduce an uncertainty-aware loss for all unlabeled points, providing additional supervised signals to optimize the network. To further extract more discriminative features, we propose an efficient regional context adaptive aggregation (RCAA) module to enhance information interaction between points. Extensive experiments on three large-scale datasets, including S3DIS, Toronto3D, and STPLS3D, demonstrate the superior performance of our method against some state-of-the-art methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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 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".