对比了点云分割中结构化与随机采样对不平衡问题的缓解效果
How Sampling Strategy Affects Imbalance Mitigation in LiDAR Segmentation: A Study of Structured vs. Random Point-Based Architectures
- 比较六种重加权和五种抗不平衡损失在两种架构上的表现
- 均匀采样在结构化架构上性能接近复杂损失，但在随机架构上差4.6%
- 采样策略与数据特性共同决定哪种方法更有效，适合自动驾驶点云研究者
LiDAR点云中的类别不平衡给自动驾驶和城市地图构建的语义分割带来挑战。尽管2D视觉有多种缓解方法，其在3D中的有效性尚不明确。我们在三个数据集（DALES、S3DIS、STPLS3D）上，用两种架构（KPConv、RandLA-Net）基准测试了六种重加权方案和五种抗不平衡损失。逆频率加权相比均匀加权性能下降最高达12%，少数类出现灾难性失败。均匀加权在结构化采样（KPConv）下性能仅比复杂损失低2%，但在随机采样（RandLA-Net）下差距最大达4.6%。损失曲面分析显示：在真实LiDAR数据上，结构化采样中不平衡比率决定曲面几何；而在合成数据上则解耦；随机采样中曲面高度敏感于数据几何，与不平衡比率无关。结果表明，采样策略、不平衡程度与数据采集特性共同影响缓解方法的有效性。
原文摘要 · Abstract (English)
Class imbalance in LiDAR point clouds poses challenges for semantic segmentation in autonomous navigation and urban mapping. While 2D vision has numerous mitigation techniques, their effectiveness in 3D remains unclear. We benchmark six reweighting schemes and five imbalance-aware losses across three datasets (DALES, S3DIS, STPLS3D) using two architectures (KPConv, RandLA-Net). Inverse-frequency weighting degrades performance by up to 12% compared to uniform weighting, with catastrophic failures in minority classes. Uniform weighting performs within 2% of complex losses for structured sampling (KPConv) but benefits less for random sampling (RandLA-Net, up to 4.6% gap). Loss landscape analysis reveals a complex interplay: for structured sampling, imbalance ratio determines landscape geometry on real LiDAR data but decouples from it on synthetic data; for random sampling, landscapes show high sensitivity to dataset geometry regardless of imbalance ratio. For the two evaluated point-based architectures, these results suggest that the interaction between sampling strategy (structured vs. random), imbalance severity, and data acquisition characteristics shapes which mitigation approaches are effective.
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