通过重建修正伪标签，提升激光雷达语义分割的半监督学习效果
RePL: Pseudo-label Refinement for Semi-supervised LiDAR Semantic Segmentation
- 用掩码重建识别并修正伪标签中的错误
- 在nuScenes和SemanticKITTI上达到最新最佳性能
- 理论证明修正条件宽松，实际应用有效
激光雷达语义分割的半监督学习常因噪声伪标签导致误差传播和确认偏差。为此，我们提出RePL框架，通过掩码重建识别并修正伪标签中的潜在错误，并设计专用训练策略。我们提供了理论分析，证明伪标签精炼在温和条件下即可生效，实验证明该条件在RePL中明确满足。在nuScenes-lidarseg和SemanticKITTI数据集上的大量评估显示，RePL显著提升伪标签质量，从而实现半监督激光雷达语义分割的最新最佳性能。
原文摘要 · Abstract (English)
Semi-supervised learning for LiDAR semantic segmentation often suffers from error propagation and confirmation bias caused by noisy pseudo-labels. To tackle this chronic issue, we introduce RePL, a novel framework that enhances pseudo-label quality by identifying and correcting potential errors in pseudo-labels through masked reconstruction, along with a dedicated training strategy. We also provide a theoretical analysis demonstrating the condition under which the pseudo-label refinement is beneficial, and empirically confirm that the condition is mild and clearly met by RePL. Extensive evaluations on the nuScenes-lidarseg and SemanticKITTI datasets show that RePL improves pseudo-label quality substantially, and in consequence, achieves the state of the art in semi-supervised LiDAR semantic segmentation.
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