HAECcity：基于超点图聚类的城市级点云开放词汇场景理解
HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering
AI总结：
针对现有开放词汇3D场景理解方法无法高效扩展到城市级数据集的问题，提出基于超点图聚类的HAEC方法，采用混合专家图Transformer骨干网络，首次将其应用于SensatUrban城市级数据集，还提出无需人工标注的合成标注流水线，为数字孪生处理开辟新路径。
摘要：
传统3D场景理解技术通常依赖人工标注的标签集，但近年来出现了一类新的开放词汇3D场景理解技术。尽管该范式在小场景上取得成功，现有方法无法高效扩展到城市级3D数据集。本文提出Hierarchical vocab-Agnostic Expert Clustering（HAEC，源自拉丁语“these”），这是一种基于超点图聚类的方法，其骨干网络采用新型混合专家图Transformer。我们将这种高可扩展方法首次应用于SensatUrban城市级数据集的开放词汇场景理解任务，还展示了完全从原始点云生成、无需人工标注的合成标注流水线。该技术有助于解锁密集城市3D场景的复杂操作，为数字孪生处理开辟新路径。
英文摘要：
Traditional 3D scene understanding techniques are generally predicated on hand-annotated label sets, but in recent years a new class of open-vocabulary 3D scene understanding techniques has emerged. Despite the success of this paradigm on small scenes, existing approaches cannot scale efficiently to city-scale 3D datasets. In this paper, we present Hierarchical vocab-Agnostic Expert Clustering (HAEC), after the latin word for 'these', a superpoint graph clustering based approach which utilizes a novel mixture of experts graph transformer for its backbone. We administer this highly scalable approach to the first application of open-vocabulary scene understanding on the SensatUrban city-scale dataset. We also demonstrate a synthetic labeling pipeline which is derived entirely from the raw point clouds with no hand-annotation. Our technique can help unlock complex operations on dense urban 3D scenes and open a new path forward in the processing of digital twins.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
