  Accurate and fast scene understanding is one of the challenging task for
autonomous driving, which requires to take full advantage of LiDAR point clouds
for semantic segmentation. In this paper, we present a \textbf{concise} and
\textbf{efficient} image-based semantic segmentation network, named
\textbf{CENet}. In order to improve the descriptive power of learned features
and reduce the computational as well as time complexity, our CENet integrates
the convolution with larger kernel size instead of MLP, carefully-selected
activation functions, and multiple auxiliary segmentation heads with
corresponding loss functions into architecture. Quantitative and qualitative
experiments conducted on publicly available benchmarks, SemanticKITTI and
SemanticPOSS, demonstrate that our pipeline achieves much better mIoU and
inference performance compared with state-of-the-art models. The code will be
available at https://github.com/huixiancheng/CENet.
