  HD map reconstruction is crucial for autonomous driving. LiDAR-based methods
are limited due to expensive sensors and time-consuming computation.
Camera-based methods usually need to perform road segmentation and view
transformation separately, which often causes distortion and missing content.
To push the limits of the technology, we present a novel framework that
reconstructs a local map formed by road layout and vehicle occupancy in the
bird's-eye view given a front-view monocular image only. We propose a
front-to-top view projection (FTVP) module, which takes the constraint of cycle
consistency between views into account and makes full use of their correlation
to strengthen the view transformation and scene understanding. In addition, we
also apply multi-scale FTVP modules to propagate the rich spatial information
of low-level features to mitigate spatial deviation of the predicted object
location. Experiments on public benchmarks show that our method achieves the
state-of-the-art performance in the tasks of road layout estimation, vehicle
occupancy estimation, and multi-class semantic estimation. For multi-class
semantic estimation, in particular, our model outperforms all competitors by a
large margin. Furthermore, our model runs at 25 FPS on a single GPU, which is
efficient and applicable for real-time panorama HD map reconstruction.
