PC-Urban Outdoordataset 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.
Date made available1 Jan 2021
PublisherIEEE DataPort
  • 3D Scene understanding from LiDAR point clouds

    Ibrahim, M., 2023, (Unpublished)

    Research output: ThesisDoctoral Thesis

    37 Downloads (Pure)

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