CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
This work addresses the need for fast and accurate scene understanding in autonomous driving, representing an incremental improvement over existing methods.
The paper tackles LiDAR semantic segmentation for autonomous driving by proposing CENet, a concise and efficient image-based network that achieves better mIoU and inference performance compared to state-of-the-art models on benchmarks like SemanticKITTI and SemanticPOSS.
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.