  Human driver can easily describe the complex traffic scene by visual system.
Such an ability of precise perception is essential for driver's planning. To
achieve this, a geometry-aware representation that quantizes the physical 3D
scene into structured grid map with semantic labels per cell, termed as 3D
Occupancy, would be desirable. Compared to the form of bounding box, a key
insight behind occupancy is that it could capture the fine-grained details of
critical obstacles in the scene, and thereby facilitate subsequent tasks. Prior
or concurrent literature mainly concentrate on a single scene completion task,
where we might argue that the potential of this occupancy representation might
obsess broader impact. In this paper, we propose OccNet, a multi-view
vision-centric pipeline with a cascade and temporal voxel decoder to
reconstruct 3D occupancy. At the core of OccNet is a general occupancy
embedding to represent 3D physical world. Such a descriptor could be applied
towards a wide span of driving tasks, including detection, segmentation and
planning. To validate the effectiveness of this new representation and our
proposed algorithm, we propose OpenOcc, the first dense high-quality 3D
occupancy benchmark built on top of nuScenes. Empirical experiments show that
there are evident performance gain across multiple tasks, e.g., motion planning
could witness a collision rate reduction by 15%-58%, demonstrating the
superiority of our method.
