  In this paper, we explore a novel point representation for 3D occupancy
prediction from multi-view images, which is named Occupancy as Set of Points.
Existing camera-based methods tend to exploit dense volume-based representation
to predict the occupancy of the whole scene, making it hard to focus on the
special areas or areas out of the perception range. In comparison, we present
the Points of Interest (PoIs) to represent the scene and propose OSP, a novel
framework for point-based 3D occupancy prediction. Owing to the inherent
flexibility of the point-based representation, OSP achieves strong performance
compared with existing methods and excels in terms of training and inference
adaptability. It extends beyond traditional perception boundaries and can be
seamlessly integrated with volume-based methods to significantly enhance their
effectiveness. Experiments on the Occ3D nuScenes occupancy benchmark show that
OSP has strong performance and flexibility. Code and models are available at
\url{https://github.com/hustvl/osp}.
