Papers › CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
Hui-Xian Cheng, Xian-Feng Han, Guo-Qiang Xiao
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Robust 3D Semantic Segmentation | SemanticKITTI-C | CENet (64x2048) | mean Corruption Error (mCE) | 103.41% | #9 of 22 | Archive leaderboard | report |
| Robust 3D Semantic Segmentation | nuScenes-C | CENet | mean Corruption Error (mCE) | 112.79% | #10 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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