  Measuring and alleviating the discrepancies between the synthetic (source)
and real scene (target) data is the core issue for domain adaptive semantic
segmentation. Though recent works have introduced depth information in the
source domain to reinforce the geometric and semantic knowledge transfer, they
cannot extract the intrinsic 3D information of objects, including positions and
shapes, merely based on 2D estimated depth. In this work, we propose a novel
Geometry-Aware Network for Domain Adaptation (GANDA), leveraging more compact
3D geometric point cloud representations to shrink the domain gaps. In
particular, we first utilize the auxiliary depth supervision from the source
domain to obtain the depth prediction in the target domain to accomplish
structure-texture disentanglement. Beyond depth estimation, we explicitly
exploit 3D topology on the point clouds generated from RGB-D images for further
coordinate-color disentanglement and pseudo-labels refinement in the target
domain. Moreover, to improve the 2D classifier in the target domain, we perform
domain-invariant geometric adaptation from source to target and unify the 2D
semantic and 3D geometric segmentation results in two domains. Note that our
GANDA is plug-and-play in any existing UDA framework. Qualitative and
quantitative results demonstrate that our model outperforms state-of-the-arts
on GTA5->Cityscapes and SYNTHIA->Cityscapes.
