用点面投影提升小数据下激光雷达语义分割精度
Point-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios
- 通过点面投影将点云映射到2D表示，挖掘互补特征
- 在小数据场景下显著提升性能，且在两个标准数据集上表现优异
- 设计符合激光雷达特性的增强方法，缓解类别不平衡问题
激光雷达点云语义分割对自动驾驶和机器人理解三维环境至关重要。现有方法虽通过不同点云表示或融合相机等外部数据取得良好效果，但常面临计算复杂度高、需大量训练数据的问题，限制了其在数据稀缺场景的泛化能力。本文提出一种基于点面投影的方法，通过将点云映射到多个有意义的2D表示，有效利用二维特征提取互补信息，仅依赖激光雷达数据实现更优特征学习。同时引入几何感知的数据增强技术，契合激光雷达传感器特性并缓解类别不平衡。实验表明，该方法在有限数据场景下性能显著提升，并在SemanticKITTI与PandaSet两个公开标准数据集上达到竞争性结果。代码已开源：https://github.com/SiMoM0/3PNet。
原文摘要 · Abstract (English)
LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics. Recent methods achieve strong performance by exploiting different point cloud representations or incorporating data from other sensors, such as cameras or external datasets. However, these approaches often suffer from high computational complexity and require large amounts of training data, limiting their generalization in data-scarce scenarios. In this paper, we improve the performance of point-based methods by effectively learning features from 2D representations through point-plane projections, enabling the extraction of complementary information while relying solely on LiDAR data. Additionally, we introduce a geometry-aware technique for data augmentation that aligns with LiDAR sensor properties and mitigates class imbalance. We implemented and evaluated our method that applies point-plane projections onto multiple informative 2D representations of the point cloud. Experiments demonstrate that this approach leads to significant improvements in limited-data scenarios, while also achieving competitive results on two publicly available standard datasets, as SemanticKITTI and PandaSet. The code of our method is available at https://github.com/SiMoM0/3PNet
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