PC-Urban Outdoor dataset for 3D Point Cloud semantic segmentation

The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances. Labelling is performed with PC-Annotate and can easily be extended by the end-users employing the same tool.The data is organized into unlabelled and labelled 3D point clouds. The unlabelled data is provided in .PCAP file format, which is the direct output format of the used Ouster LiDAR sensor. Raw frames are extracted from the recorded .PCAP files in the form of Ply and Excel files using the Ouster Studio Software. Labelled 3D point cloud data consists of registered or raw point clouds. A labelled point cloud is a combination of Ply, Excel, Labels and Summary files. A point cloud in Ply file contains X, Y, Z values along with color information. An Excel file contains X, Y, Z values, Intensity, Reflectivity, Ring, Noise, and Range of each point. These attributes can be useful in semantic segmentation using deep learning algorithms. The Label and Label Summary files have been explained in the previous section. Our one GB raw data contains nearly 1,300 raw frames, whereas 66,425 frames are provided in the dataset, each comprising 65,536 points. Hence, 4.3 billion points captured with the Ouster LiDAR sensor are provided. Annotation of 25 general outdoor classes is provided, which include car, building, bridge, tree, road, letterbox, traffic signal, light-pole, rubbish bin, cycles, motorcycle, truck, bus, bushes, road sign board, advertising board, road divider, road lane, pedestrians, side-path, wall, bus stop, water, zebra-crossing, and background. With the released data, a total of 143 scenes are annotated which include both raw and registered frames.

本研究所提出的数据集，命名为PC-Urban（城市点云），采用Ouster LiDAR传感器，具备64个通道进行采集。该传感器被安装在SUV车型上，穿梭于澳大利亚西澳大利亚州（WA）珀斯市中心。该数据集包含超过43亿个点，对应于66,000个传感器帧。标注数据被组织成已注册和原始点云帧，其中前者包含不同数量的连续注册帧。数据集中提供25个类别标签，涵盖2300万个点及5,000个实例。标注工作由PC-Annotate完成，并可通过相同工具轻松扩展。数据被组织为无标注和标注的3D点云。无标注数据以.PCAP文件格式提供，这是所使用Ouster LiDAR传感器的直接输出格式。原始帧通过Ouster Studio软件从记录的.PCAP文件中提取，并以Ply和Excel文件的形式呈现。标注的3D点云数据包括注册或原始点云。Ply文件中的点云包含X、Y、Z值以及颜色信息。Excel文件则包含每个点的X、Y、Z值、强度、反射率、环数、噪声和距离。这些属性在深度学习算法进行语义分割时可能非常有用。标签和标签摘要文件在先前章节中已进行解释。我们的1GB原始数据包含近1,300个原始帧，而数据集中提供了66,425个帧，每个帧包含65,536个点。因此，使用Ouster LiDAR传感器捕获的43亿个点得以提供。提供了25个通用户外类别的标注，包括汽车、建筑物、桥梁、树木、道路、信箱、交通信号、路灯、垃圾桶、自行车、摩托车、卡车、公共汽车、灌木丛、路标、广告牌、道路隔离带、道路车道、行人、侧道、墙壁、公交车站、水域、斑马线以及背景。随着数据的发布，共计143个场景被标注，包括原始和注册帧。