  Given an image or a video captured from a monocular camera, amodal layout
estimation is the task of predicting semantics and occupancy in bird's eye
view. The term amodal implies we also reason about entities in the scene that
are occluded or truncated in image space. While several recent efforts have
tackled this problem, there is a lack of standardization in task specification,
datasets, and evaluation protocols. We address these gaps with AutoLay, a
dataset and benchmark for amodal layout estimation from monocular images.
AutoLay encompasses driving imagery from two popular datasets: KITTI and
Argoverse. In addition to fine-grained attributes such as lanes, sidewalks, and
vehicles, we also provide semantically annotated 3D point clouds. We implement
several baselines and bleeding edge approaches, and release our data and code.
