  The bird's-eye-view (BEV) representation allows robust learning of multiple
tasks for autonomous driving including road layout estimation and 3D object
detection. However, contemporary methods for unified road layout estimation and
3D object detection rarely handle the class imbalance of the training dataset
and multi-class learning to reduce the total number of networks required. To
overcome these limitations, we propose a unified model for road layout
estimation and 3D object detection inspired by the transformer architecture and
the CycleGAN learning framework. The proposed model deals with the performance
degradation due to the class imbalance of the dataset utilizing the focal loss
and the proposed dual cycle loss. Moreover, we set up extensive learning
scenarios to study the effect of multi-class learning for road layout
estimation in various situations. To verify the effectiveness of the proposed
model and the learning scheme, we conduct a thorough ablation study and a
comparative study. The experiment results attest the effectiveness of our
model; we achieve state-of-the-art performance in both the road layout
estimation and 3D object detection tasks.
