Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%.

陈旧或粗略的街道设施清单可能会误导规划者对交通基础设施的改造和升级，从而对交通安全构成潜在威胁。以前的研究已经使用点云或街景图像 (SVI) 来进行街道家具库存，但在语义丰富度、定位准确性和工作效率之间仍存在差距。因此，本文提出了一种有效的管道，将 SVI 和点云相结合，用于街道家具的库存。提议的管道包括三个步骤：（1）在 SVI 上应用现成的街道设施检测模型以生成二维（2D）建议，然后相应地裁剪三维（3D）点云平截头体；(2) 使用多任务神经网络为每个截锥体预测实例掩码和实例 3D 边界框；(3) 来自相邻视角的截锥体通过多对象跟踪进行关联和融合，之后以对象为中心的实例分割输出具有 3D 位置和语义标签的最终街道设施。该管道在上海和武汉收集的数据集上进行了验证，生成了九类的组件级街道家具库存。上海和武汉的实例级平均查全率和查准率分别达到86.4%、80.9%和83.2%、87.8%，点级查全率、查准率、加权覆盖率均超过73.7%。之后，以对象为中心的实例分割输出带有 3D 位置和语义标签的最终街道设施。该管道在上海和武汉收集的数据集上进行了验证，生成了九类的组件级街道家具库存。上海和武汉的实例级平均查全率和查准率分别达到86.4%、80.9%和83.2%、87.8%，点级查全率、查准率、加权覆盖率均超过73.7%。之后，以对象为中心的实例分割输出带有 3D 位置和语义标签的最终街道设施。该管道在上海和武汉收集的数据集上进行了验证，生成了九类的组件级街道家具库存。上海和武汉的实例级平均查全率和查准率分别达到86.4%、80.9%和83.2%、87.8%，点级查全率、查准率、加权覆盖率均超过73.7%。