  Understanding semantic scene segmentation of urban scenes captured from the
Unmanned Aerial Vehicles (UAV) perspective plays a vital role in building a
perception model for UAV. With the limitations of large-scale densely labeled
data, semantic scene segmentation for UAV views requires a broad understanding
of an object from both its top and side views. Adapting from well-annotated
autonomous driving data to unlabeled UAV data is challenging due to the
cross-view differences between the two data types. Our work proposes a novel
Cross-View Adaptation (CROVIA) approach to effectively adapt the knowledge
learned from on-road vehicle views to UAV views. First, a novel geometry-based
constraint to cross-view adaptation is introduced based on the geometry
correlation between views. Second, cross-view correlations from image space are
effectively transferred to segmentation space without any requirement of paired
on-road and UAV view data via a new Geometry-Constraint Cross-View (GeiCo)
loss. Third, the multi-modal bijective networks are introduced to enforce the
global structural modeling across views. Experimental results on new cross-view
adaptation benchmarks introduced in this work, i.e., SYNTHIA to UAVID and GTA5
to UAVID, show the State-of-the-Art (SOTA) performance of our approach over
prior adaptation methods
