  We propose an end-to-end network that takes a single perspective RGB image of
a complex road scene as input, to produce occlusion-reasoned layouts in
perspective space as well as a top-view parametric space. In contrast to prior
works that require dense supervision such as semantic labels in perspective
view, the only human annotations required by our method are for parametric
attributes that are cheaper and less ambiguous to obtain. To solve this
challenging task, our design is comprised of modules that incorporate inductive
biases to learn occlusion-reasoning, geometric transformation and semantic
abstraction, where each module may be supervised by appropriately transforming
the parametric annotations. We demonstrate how our design choices and proposed
deep supervision help achieve accurate predictions and meaningful
representations. We validate our approach on two public datasets, KITTI and
NuScenes, to achieve state-of-the-art results with considerably lower human
supervision.
