  Semantic segmentation in autonomous driving has been undergoing an evolution
from sparse point segmentation to dense voxel segmentation, where the objective
is to predict the semantic occupancy of each voxel in the concerned 3D space.
The dense nature of the prediction space has rendered existing efficient
2D-projection-based methods (e.g., bird's eye view, range view, etc.)
ineffective, as they can only describe a subspace of the 3D scene. To address
this, we propose a cylindrical tri-perspective view to represent point clouds
effectively and comprehensively and a PointOcc model to process them
efficiently. Considering the distance distribution of LiDAR point clouds, we
construct the tri-perspective view in the cylindrical coordinate system for
more fine-grained modeling of nearer areas. We employ spatial group pooling to
maintain structural details during projection and adopt 2D backbones to
efficiently process each TPV plane. Finally, we obtain the features of each
point by aggregating its projected features on each of the processed TPV planes
without the need for any post-processing. Extensive experiments on both 3D
occupancy prediction and LiDAR segmentation benchmarks demonstrate that the
proposed PointOcc achieves state-of-the-art performance with much faster speed.
Specifically, despite only using LiDAR, PointOcc significantly outperforms all
other methods, including multi-modal methods, with a large margin on the
OpenOccupancy benchmark. Code: https://github.com/wzzheng/PointOcc.
