This paper addresses the critical challenge of domain adaptation for LiDAR-based semantic segmentation, particularly the significant density disparities that emerge when transferring models from synthetic to real-world environments. We present DA2-LiDAR, a novel density-adaptive domain adaptation framework that bridges domain gaps through the construction of intermediate domains with density-varying point distributions. Our approach employs a simple yet effective masking strategy that systematically reduces density discrepancies between domains while extracting more effective supervisory signals, as well as preserving critical semantic information. The framework consists of three key components: (1) a Density Adaptation Module that establishes a continuous spectrum of intermediate domains through dataset-agnostic masking operations; (2) a Contextual Consistency Module that enforces relational coherence across differently masked variants of the same scan at varying degrees, providing additional supervision signals, enhancing the model's ability to extract features; and (3) a Semantic Preservation Module that mitigates information loss in heavily masked scans by reconstructing domain-specific data distributions. Extensive experiments on synthetic-to-real and other benchmarks demonstrate that DA2-LiDAR consistently outperforms state-of-the-art methods, achieving significant improvements in cross-domain generalization without requiring dataset-specific prior knowledge or introducing computational overhead.
Chen et al. (Thu,) studied this question.