用低成本激光雷达实现高精度自动驾驶场景分割
Guided Model-based LiDAR Super-Resolution for Resource-Efficient Automotive scene Segmentation
- 端到端联合优化，用语义信息指导点云超分辨率
- 仅用16通道数据，分割效果接近64通道高端设备
- 轻量架构适合车载部署，兼顾精度与资源效率
高分辨率激光雷达数据对自动驾驶的3D语义分割至关重要，但高端传感器成本高昂，难以大规模应用。相比之下，16通道低成本激光雷达生成的点云稀疏，导致分割精度下降。为此，我们提出首个端到端框架，同时完成激光雷达超分辨率（SR）与语义分割。训练时采用联合优化，使超分辨率模块能利用语义线索并保留细小物体的细节，尤其提升小目标类别表现。引入新型超分辨率损失函数，引导网络聚焦感兴趣区域。所提轻量级、基于模型的超分辨率架构参数量远低于现有方法，且易于与分割网络集成。实验表明，该方法在16通道数据上的分割性能可媲美使用高成本64通道激光雷达数据的模型。
原文摘要 · Abstract (English)
High-resolution LiDAR data plays a critical role in 3D semantic segmentation for autonomous driving, but the high cost of advanced sensors limits large-scale deployment. In contrast, low-cost sensors such as 16-channel LiDAR produce sparse point clouds that degrade segmentation accuracy. To overcome this, we introduce the first end-to-end framework that jointly addresses LiDAR super-resolution (SR) and semantic segmentation. The framework employs joint optimization during training, allowing the SR module to incorporate semantic cues and preserve fine details, particularly for smaller object classes. A new SR loss function further directs the network to focus on regions of interest. The proposed lightweight, model-based SR architecture uses significantly fewer parameters than existing LiDAR SR approaches, while remaining easily compatible with segmentation networks. Experiments show that our method achieves segmentation performance comparable to models operating on high-resolution and costly 64-channel LiDAR data.
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