  Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles
understand the surrounding environment only from images, including static
elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians).
However, the high cost of annotation procedures of full-supervised methods
limits the capability of the visual BEV semantic segmentation, which usually
needs HD maps, 3D object bounding boxes, and camera extrinsic matrixes. In this
paper, we present a novel semi-supervised framework for visual BEV semantic
segmentation to boost performance by exploiting unlabeled images during the
training. A consistency loss that makes full use of unlabeled data is then
proposed to constrain the model on not only semantic prediction but also the
BEV feature. Furthermore, we propose a novel and effective data augmentation
method named conjoint rotation which reasonably augments the dataset while
maintaining the geometric relationship between the front-view images and the
BEV semantic segmentation. Extensive experiments on the nuScenes and Argoverse
datasets show that our semi-supervised framework can effectively improve
prediction accuracy. To the best of our knowledge, this is the first work that
explores improving visual BEV semantic segmentation performance using unlabeled
data. The code is available at https://github.com/Junyu-Z/Semi-BEVseg
