3D semantic segmentation plays a pivotal role in autonomous driving and road infrastructure analysis, yet state-of-the-art 3D models are prone to severe domain shift when deployed across different datasets. In this paper, we propose an Unsupervised Domain Adaptation approach where a 3D segmentation model is trained on the target dataset using pseudo-labels generated by a novel multi-view projection framework. Our approach first aligns Lidar scans into coherent 3D scenes and renders them from multiple virtual camera poses to create large-scale synthetic 2D semantic segmentation datasets in various modalities. The generated datasets are used to train an ensemble of 2D segmentation models in point cloud view domain on each modality. During inference, the models process a large amount of views per scene; the resulting logits are back-projected to 3D with a depth-aware voting scheme to generate final point-wise labels. These labels are then used to fine-tune a 3D segmentation model in the target domain. We evaluate our approach Real-to-Real on the nuScenes and SemanticKITTI datasets. We also evaluate it Simulation-to-Real with the SynLidar dataset. Our contributions are a novel method that achieves state-of-the-art results in Real-to-Real Unsupervised Domain Adaptation, and we also demonstrate an application of our method to segment rare classes, for which target 3D annotations are not available, by only using 2D annotations for those classes and leveraging 3D annotations for other classes in a source domain.

翻译：三维语义分割在自动驾驶与道路基础设施分析中扮演着关键角色，然而当前最先进的三维模型在不同数据集间部署时极易受到严重域偏移的影响。本文提出一种无监督域自适应方法，通过新颖的多视角投影框架生成伪标签，在目标数据集上训练三维分割模型。该方法首先将激光雷达扫描数据对齐为连贯的三维场景，并从多个虚拟相机位姿进行渲染，以创建多种模态的大规模合成二维语义分割数据集。生成的数据集用于在点云视图域中训练针对各模态的二维分割模型集成。在推理阶段，模型处理每个场景的大量视角；所得逻辑值通过深度感知投票方案反投影至三维空间，生成最终的点级标签。这些标签随后用于在目标域中对三维分割模型进行微调。我们在nuScenes和SemanticKITTI数据集上进行了真实到真实场景的评估，并利用SynLidar数据集进行了仿真到真实场景的评估。本研究的贡献在于提出了一种在真实到真实无监督域自适应中达到最先进性能的新方法，同时展示了该方法在分割稀有类别上的应用：仅使用这些类别的二维标注，并利用源域中其他类别的三维标注，即可对目标域中缺乏三维标注的稀有类别实现分割。