论文标题
CENET：迈向自动驾驶的简洁有效的激光雷达语义细分
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
论文作者
论文摘要
准确而快速的场景理解是自动驾驶的挑战性任务之一，它需要充分利用LiDar Point Clouds进行语义细分。在本文中，我们提出了一个\ textbf {concise}和\ textbf {有效}基于图像的语义分割网络，名为\ textbf {cenet}。为了提高学习特征的描述能力并降低计算和时间复杂性，我们的CENET将卷积与较大的内核大小而不是MLP，精心挑选的激活功能以及多个辅助分割头以及具有相应损耗函数的多个辅助分割头整合在一起。定量和定性实验是根据公开可用的基准测试，Semantickitti和Semanticposs进行的，这表明我们的管道与最先进的模型相比，我们的管道取得了更好的MIOU和推理性能。该代码将在https://github.com/huixiancheng/cenet上找到。
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.
