用激光雷达强度图生成2D语义分割，反投影到3D点云做无监督标注。
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation
- 将激光雷达点云转为强度图，用预训练2D模型分割。
- 通过投票机制融合2D标签，得到3D点云伪标签。
- 无需3D标注或相机数据，适合域自适应场景。
3D激光雷达点云语义分割对自动驾驶和基础设施管理至关重要，通常依赖大量标注数据，且易受领域偏移影响。本文提出一种新方法：利用对齐的场景和先进的2D分割模型，无需直接进行3D标注，也无需在推理时依赖额外模态（如相机图像）。具体地，将激光雷达扫描数据按传感器强度着色生成2D视图，使用在摄像头数据上预训练的2D语义分割模型对其进行分割；再将分割结果回投影至3D点云，并采用简单的基于投票的估计器融合每个3D点对应的标签。主要贡献在于构建了一套不依赖先验3D标注且无需其他模态进行推理的3D语义分割流程，可用于伪标签生成。通过全面的消融实验，验证了生成伪标签在无监督域自适应任务中的潜力。
原文摘要 · Abstract (English)
Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive annotated datasets and faces the problem of domain shifts. We introduce a new 3D semantic segmentation pipeline that leverages aligned scenes and state-of-the-art 2D segmentation methods, avoiding the need for direct 3D annotation or reliance on additional modalities such as camera images at inference time. Our approach generates 2D views from LiDAR scans colored by sensor intensity and applies 2D semantic segmentation to these views using a camera-domain pretrained model. The segmented 2D outputs are then back-projected onto the 3D points, with a simple voting-based estimator that merges the labels associated to each 3D point. Our main contribution is a global pipeline for 3D semantic segmentation requiring no prior 3D annotation and not other modality for inference, which can be used for pseudo-label generation. We conduct a thorough ablation study and demonstrate the potential of the generated pseudo-labels for the Unsupervised Domain Adaptation task.
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