  Camera-only Bird's Eye View (BEV) has demonstrated great potential in
environment perception in a 3D space. However, most existing studies were
conducted under a supervised setup which cannot scale well while handling
various new data. Unsupervised domain adaptive BEV, which effective learning
from various unlabelled target data, is far under-explored. In this work, we
design DA-BEV, the first domain adaptive camera-only BEV framework that
addresses domain adaptive BEV challenges by exploiting the complementary nature
of image-view features and BEV features. DA-BEV introduces the idea of query
into the domain adaptation framework to derive useful information from
image-view and BEV features. It consists of two query-based designs, namely,
query-based adversarial learning (QAL) and query-based self-training (QST),
which exploits image-view features or BEV features to regularize the adaptation
of the other. Extensive experiments show that DA-BEV achieves superior domain
adaptive BEV perception performance consistently across multiple datasets and
tasks such as 3D object detection and 3D scene segmentation.
