  3D occupancy prediction (Occ) is a rapidly rising challenging perception task
in the field of autonomous driving which represents the driving scene as
uniformly partitioned 3D voxel grids with semantics. Compared to 3D object
detection, grid perception has great advantage of better recognizing
irregularly shaped, unknown category, or partially occluded general objects.
However, existing 3D occupancy networks (occnets) are both computationally
heavy and label-hungry. In terms of model complexity, occnets are commonly
composed of heavy Conv3D modules or transformers on the voxel level. In terms
of label annotations requirements, occnets are supervised with large-scale
expensive dense voxel labels. Model and data inefficiency, caused by excessive
network parameters and label annotations requirement, severely hinder the
onboard deployment of occnets. This paper proposes an efficient 3d occupancy
network (EFFOcc), that targets the minimal network complexity and label
requirement while achieving state-of-the-art accuracy. EFFOcc only uses simple
2D operators, and improves Occ accuracy to the state-of-the-art on multiple
large-scale benchmarks: Occ3D-nuScenes, Occ3D-Waymo, and
OpenOccupancy-nuScenes. On Occ3D-nuScenes benchmark, EFFOcc has only 18.4M
parameters, and achieves 50.46 in terms of mean IoU (mIoU), to our knowledge,
it is the occnet with minimal parameters compared with related occnets.
Moreover, we propose a two-stage active learning strategy to reduce the
requirements of labelled data. Active EFFOcc trained with 6\% labelled voxels
achieves 47.19 mIoU, which is 95.7% fully supervised performance. The proposed
EFFOcc also supports improved vision-only occupancy prediction with the aid of
region-decomposed distillation. Code and demo videos will be available at
https://github.com/synsin0/EFFOcc.
