  In this work, we research and evaluate end-to-end learning of monocular
semantic-metric occupancy grid mapping from weak binocular ground truth. The
network learns to predict four classes, as well as a camera to bird's eye view
mapping. At the core, it utilizes a variational encoder-decoder network that
encodes the front-view visual information of the driving scene and subsequently
decodes it into a 2-D top-view Cartesian coordinate system. The evaluations on
Cityscapes show that the end-to-end learning of semantic-metric occupancy grids
outperforms the deterministic mapping approach with flat-plane assumption by
more than 12% mean IoU. Furthermore, we show that the variational sampling with
a relatively small embedding vector brings robustness against vehicle dynamic
perturbations, and generalizability for unseen KITTI data. Our network achieves
real-time inference rates of approx. 35 Hz for an input image with a resolution
of 256x512 pixels and an output map with 64x64 occupancy grid cells using a
Titan V GPU.
