Class-Incremental Semantic Segmentation for Mobile Laser Scanning Point Clouds Using Feature Representation Preservation and Loss Cross-Coupling
Bibliographic record
Abstract
Significant progress has been made in the semantic segmentation of mobile laser scanning (MLS) point clouds based on deep learning. However, the segmentation classes of deep learning models depend on the label classes of the source point clouds used for training, which makes it difficult to generalize the models to target point clouds with novel classes. In addition, retraining models using complete class label datasets is time-consuming, and the source point clouds are often unavailable or occupy a large amount of storage space. In this paper, we propose a new class-incremental semantic segmentation framework for MLS point clouds. Firstly, to prevent catastrophic forgetting of original class knowledge when the model learns novel classes, we design a feature representation preservation-based knowledge distillation module to maintain the encoding ability of the target models for original classes. Then, to further separate novel classes from the original background classes, we introduce a background shift mechanism based on loss cross-coupling and pseudo-label collaborative training, which adaptively balances the model plasticity when learning novel class knowledge. Finally, we conducted extensive experiments on two benchmark datasets (Paris-Lille-3D and Toronto-3D), and our proposed method achieved impressive results, which indicate that the proposed framework could effectively achieve class-incremental semantic segmentation for MLS point clouds.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".