Context-Aware Feature Adaptation for Mitigating Negative Transfer in 3D LiDAR Semantic Segmentation

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 16; p. 2825
• Main Authors: El Mendili, Lamiae, Daniel, Sylvie, Badard, Thierry
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.08.2025
• Subjects: Ablation; Adaptation; Bridges; Datasets; Image segmentation; Lidar; Regularization methods; Semantic segmentation; Semantics; Three dimensional models
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17162825

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.