We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse).

翻译：我们提出了两个多模态全景3D室外（MPO）数据集，用于六类语义地点分类：森林、海岸、住宅区、城市区域以及室内/室外停车场。第一个数据集包含650个静态全景扫描，采用FARO激光扫描仪获取密集（9,000,000点）3D彩色和反射率点云，并配有同步彩色图像。第二个数据集包含34,200个实时全景扫描，在驾驶汽车时使用Velodyne激光扫描仪获取稀疏（70,000点）3D反射率点云。这些数据集在日本福冈市采集，并在[1]、[2]中公开。此外，我们比较了多种语义地点分类方法，最佳结果分别为96.42%（密集数据）和89.67%（稀疏数据）。