Context-Aware Feature Adaptation for Mitigating Negative Transfer in 3D LiDAR Semantic Segmentation
Bibliographic record
Abstract
Semantic segmentation of 3D LiDAR point clouds is crucial for autonomous driving and urban modeling but requires extensive labeled data. Unsupervised domain adaptation from synthetic to real data offers a promising solution, yet faces the challenge of negative transfer, particularly due to context shifts between domains. This paper introduces Context-Aware Feature Adaptation, a novel approach to mitigate negative transfer in 3D unsupervised domain adaptation. The proposed approach disentangles object-specific and context-specific features, refines source context features through cross-attention with target information, and adaptively fuses the results. We evaluate our approach on challenging synthetic-to-real adaptation scenarios, demonstrating consistent improvements over state-of-the-art domain adaptation methods with up to 7.9% improvement in classes subject to context shift. Our comprehensive domain shift analysis reveals a positive correlation between context shift magnitude and performance improvement. Extensive ablation studies and visualizations further validate the efficacy in handling context shift for 3D semantic segmentation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".