Distribution-aware contrastive learning for domain adaptation in 3D LiDAR segmentation
Bibliographic record
Abstract
Semantic segmentation of 3D LiDAR point clouds is very important for applications like autonomous driving and digital twins of cities. However, current deep learning models suffer from a significant generalization gap. Unsupervised Domain Adaptation methods have recently emerged to tackle this issue. While domain-invariant feature learning using Maximum Mean Discrepancy has shown promise for images due to its simplicity, its application remains unexplored in outdoor mobile mapping point clouds. Moreover, previous methods don’t consider the class information, which can lead to suboptimal adaptation performance. We propose a new approach—Contrastive Maximum Mean Discrepancy—to maximize intra-class domain alignment and minimize inter-class domain discrepancy, and integrate it into a 3D semantic segmentation model for LiDAR point clouds. The evaluation of our method with large-scale UDA datasets shows that it surpasses state-of-the-art UDA approaches for 3D LiDAR point clouds. CMMD is a promising UDA approach with strong potential for point cloud semantic segmentation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".