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 will be publicly available.

本文提出了一种新颖的半监督框架，用于通过利用未标记的图像在训练过程中提高视觉鸟瞰（BEV）语义分割的性能，包括使用一致性损失约束模型在语义预测和 BEV 特征上，以及一种名为联合旋转的新颖且有效的数据增强方法，在保持前视图像与 BEV 语义分割之间的几何关系的同时扩充数据集。对 nuScenes 和 Argoverse 数据集进行的大量实验证明了我们的半监督框架可以有效提高预测准确性。据我们所知，这是第一项利用未标记数据改进视觉 BEV 语义分割性能的工作，代码将公开提供。