  The autonomous car must recognize the driving environment quickly for safe
driving. As the Light Detection And Range (LiDAR) sensor is widely used in the
autonomous car, fast semantic segmentation of LiDAR point cloud, which is the
point-wise classification of the point cloud within the sensor framerate, has
attracted attention in recognition of the driving environment. Although the
voxel and fusion-based semantic segmentation models are the state-of-the-art
model in point cloud semantic segmentation recently, their real-time
performance suffer from high computational load due to high voxel resolution.
In this paper, we propose the fast voxel-based semantic segmentation model
using Point Convolution and 3D Sparse Convolution (PCSCNet). The proposed model
is designed to outperform at both high and low voxel resolution using point
convolution-based feature extraction. Moreover, the proposed model accelerates
the feature propagation using 3D sparse convolution after the feature
extraction. The experimental results demonstrate that the proposed model
outperforms the state-of-the-art real-time models in semantic segmentation of
SemanticKITTI and nuScenes, and achieves the real-time performance in LiDAR
point cloud inference.
