GADA-SegNet: gated attentive domain adaptation network for semantic segmentation of LiDAR point clouds

We propose GADA-SegNet, a gated attentive domain adaptation network for semantic segmentation of LiDAR point clouds. Unlike most of existing methods that learn fully from point-wise annotations, our GADA-SegNet attempts to learn from labeled data first and then transfer itself smoothly to unlabeled data. We have three key contributions to bridge the domain gap between the labeled data and the unlabeled yet unseen data. First, we design a new gated connection module that can filter out noise and domain-private features from the low-level features, for better high- and low-level feature fusion. Second, we introduce a multi-scale attention module that can ease the large-scale variation of objects and class imbalance in complex scenes to reduce the class-level domain gap. Third, we develop a shared domain discriminator to implement the class-level domain discrimination for large-scale LiDAR point clouds. Experiments on both synthetic-to-real and real-to-real scenarios show clear improvements of our GADA-SegNet over its competitors.