Dual geometric perception for cross-domain road segmentation

Road segmentation plays an important role in navigation systems and autonomous driving. However, many methods in road segmentation are based on supervised learning and suffer from performance degradation in the real world. There is a certain domain gap (distribution shift problem) between the source domain (training data) and the target domain (testing data). In this paper, we propose a Dual-Geometric Perception (DGP) approach for cross-domain road segmentation, which jointly uses semantic and dual-geometric information to learn the domain-invariant feature for road segmentation. First, we propose an RGB-N dual stream network structure, which effectively fuses normal vector information and RGB information to reduce domain gap. Moreover, a dual geometric adversarial learning strategy is proposed to utilize depth-aware and normal vector features to perform better domain alignment. Furthermore, a self-training learning strategy is used to further improve the model’s generalizability in the target domain. Extensive experiments demonstrate that our proposed DGP achieves superior performance on lane-to-lane and lane-to-sidewalk road domain adaptation tasks. •A Dual-Geometric Perception (DGP) approach for cross-domain road segmentation is proposed.•An RGB-N dual stream network structure is proposed.•A dual geometric adversarial learning and a self-training learning strategy are proposed.