构建两个全景3D室外数据集，用于场景分类。
Multi-modal panoramic 3D outdoor datasets for place categorization
- 采集650个密集点云与34200个稀疏点云，含颜色和反射率信息。
- 在密集与稀疏数据上分别达到96.42%和89.67%的分类准确率。
- 适合做环境感知、自动驾驶中的场景理解研究者使用。
我们提出了两个用于语义场景分类的多模态全景3D室外（MPO）数据集，包含六类场景：森林、海岸、住宅区、城区及室内外停车场。首个数据集包含650个静态全景扫描，由FARO激光扫描仪获取，密度高达900万点的彩色与反射率点云，同步拍摄彩色图像。第二个数据集包含34,200个实时全景扫描，由Velodyne激光扫描仪在驾驶过程中获取，点云稀疏（约7万点），仅含反射率信息。所有数据均在日本福冈市采集，已公开发布于[1]、[2]。我们对比了多种语义场景分类方法，最佳结果分别为96.42%（密集）和89.67%（稀疏）。
原文摘要 · Abstract (English)
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).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。