Distribution-aware contrastive learning for domain adaptation in 3D LiDAR segmentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 it