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We propose an end-to-end network that takes a single perspective RGB image ofa complex road scene as input, to produce occlusion-reasoned layouts inperspective space as well as a parametric bird's-eye-view (BEV) space. Incontrast to prior works that require dense supervision such as semantic labelsin perspective view, our method only requires human annotations for parametricattributes that are cheaper and less ambiguous to obtain. To solve thischallenging task, our design is comprised of modules that incorporate inductivebiases to learn occlusion-reasoning, geometric transformation and semanticabstraction, where each module may be supervised by appropriately transformingthe parametric annotations. We demonstrate how our design choices and proposeddeep supervision help achieve meaningful representations and accuratepredictions. We validate our approach on two public datasets, KITTI andNuScenes, to achieve state-of-the-art results with considerably less humansupervision.

Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts