  Generating annotations for bird's-eye-view (BEV) segmentation presents
significant challenges due to the scenes' complexity and the high manual
annotation cost. In this work, we address these challenges by leveraging the
abundance of unlabeled data available. We propose the Perspective Cue Training
(PCT) framework, a novel training framework that utilizes pseudo-labels
generated from unlabeled perspective images using publicly available semantic
segmentation models trained on large street-view datasets. PCT applies a
perspective view task head to the image encoder shared with the BEV
segmentation head, effectively utilizing the unlabeled data to be trained with
the generated pseudo-labels. Since image encoders are present in nearly all
camera-based BEV segmentation architectures, PCT is flexible and applicable to
various existing BEV architectures. PCT can be applied to various settings
where unlabeled data is available. In this paper, we applied PCT for
semi-supervised learning (SSL) and unsupervised domain adaptation (UDA).
Additionally, we introduce strong input perturbation through Camera Dropout
(CamDrop) and feature perturbation via BEV Feature Dropout (BFD), which are
crucial for enhancing SSL capabilities using our teacher-student framework. Our
comprehensive approach is simple and flexible but yields significant
improvements over various baselines for SSL and UDA, achieving competitive
performances even against the current state-of-the-art.
