通过高低时序敏感表征，提升自动驾驶点云语义分割的半监督性能。
HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving
- 区分近距稳定与远距多变物体，分别学习高低时序敏感特征
- 在SemanticKITTI和nuScenes上超越现有半监督方法，接近多模态精度
- 适合需要减少标注成本的自动驾驶点云分割场景
LiDAR点云语义分割在自动驾驶中至关重要。近年来，半监督方法因显著降低标注成本而受到关注。现有方法多聚焦于点云空间分布或短期时序信息（如仅相邻两帧），忽略了驾驶场景中丰富的长期时序特性。观察发现：行驶过程中近处物体（如道路、车辆）相对稳定，远处物体则类别与形状变化较大。这一现象反映在LiDAR数据中表现为近处物体时序敏感度低，远处物体高。为此，我们提出HiLoTs，从连续LiDAR帧中学习高、低时序敏感表征，并通过交叉注意力机制进行增强与融合。同时采用教师-学生框架对齐有标签与无标签分支的表示，有效利用大量未标注数据。在SemanticKITTI和nuScenes数据集上的实验表明，所提方法优于当前最优半监督方法，性能接近LiDAR+Camera多模态方案。代码已开源。
原文摘要 · Abstract (English)
LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. Code is available on https://github.com/rdlin118/HiLoTs
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