  Monocular 3D detection is a challenging task due to the lack of accurate 3D
information. Existing approaches typically rely on geometry constraints and
dense depth estimates to facilitate the learning, but often fail to fully
exploit the benefits of three-dimensional feature extraction in frustum and 3D
space. In this paper, we propose \textbf{OccupancyM3D}, a method of learning
occupancy for monocular 3D detection. It directly learns occupancy in frustum
and 3D space, leading to more discriminative and informative 3D features and
representations. Specifically, by using synchronized raw sparse LiDAR point
clouds, we define the space status and generate voxel-based occupancy labels.
We formulate occupancy prediction as a simple classification problem and design
associated occupancy losses. Resulting occupancy estimates are employed to
enhance original frustum/3D features. As a result, experiments on KITTI and
Waymo open datasets demonstrate that the proposed method achieves a new state
of the art and surpasses other methods by a significant margin. Codes and
pre-trained models will be available at
\url{https://github.com/SPengLiang/OccupancyM3D}.
