  LiDAR point cloud semantic segmentation enables the robots to obtain
fine-grained semantic information of the surrounding environment. Recently,
many works project the point cloud onto the 2D image and adopt the 2D
Convolutional Neural Networks (CNNs) or vision transformer for LiDAR point
cloud semantic segmentation. However, since more than one point can be
projected onto the same 2D position but only one point can be preserved, the
previous 2D image-based segmentation methods suffer from inevitable quantized
information loss. To avoid quantized information loss, in this paper, we
propose a novel spherical frustum structure. The points projected onto the same
2D position are preserved in the spherical frustums. Moreover, we propose a
memory-efficient hash-based representation of spherical frustums. Through the
hash-based representation, we propose the Spherical Frustum sparse Convolution
(SFC) and Frustum Fast Point Sampling (F2PS) to convolve and sample the points
stored in spherical frustums respectively. Finally, we present the Spherical
Frustum sparse Convolution Network (SFCNet) to adopt 2D CNNs for LiDAR point
cloud semantic segmentation without quantized information loss. Extensive
experiments on the SemanticKITTI and nuScenes datasets demonstrate that our
SFCNet outperforms the 2D image-based semantic segmentation methods based on
conventional spherical projection. The source code will be released later.
