Cenet: Toward Concise and Efficient Lidar Semantic Segmentation for Autonomous Driving
Hui-Xian Cheng, Xian-Feng Han, Guoqiang Xiao
Abstract
Accurate and fast scene understanding is one of the chal-lenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmen-tation. In this paper, we present a concise and efficient image-based semantic segmentation network, named CENet. In order to improve the descriptive power of learned features and reduce the computational as well as time complex-ity, our CEN et integrates the convolution with larger ker-nel size instead of MLP, carefully-selected activation functions, and multiple auxiliary segmentation heads with cor-responding loss functions into architecture. Quantitative and qualitative experiments conducted on publicly available benchmarks, SemanticKITTI and SemanticPOSS, demon-strate that our pipeline achieves much better mIoU and in-ference performance compared with state-of-the-art models. The code will be available at https://github.com/huixiancheng/CENet.