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 this https URL

本文介绍了一种名为 CENet 的图像语义分割网络，采用卷积而非 MLP，精选的激活函数和多个辅助分割头来提高特征描述能力和降低计算时间复杂度，使用公开的基准测试 SemanticKITTI 和 SemanticPOSS 进行的实验表明，与现有模型相比，我们的模型在 mIoU 和推理性能方面均表现更好。