…In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class. Citation Citation. More information on the task and the metric, you can find in our publication related to the task: @article{chen2021ral, title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach
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Large-scale and open-access LiDAR dataset intended for the evaluation of real-time semantic segmentation algorithms.
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DELIVER is an arbitrary-modal segmentation benchmark, covering Depth, LiDAR, multiple Views, Events, and RGB. It is designed for the tasks of arbitrary-modal semantic segmentation.
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…data includes synchronized and aligned samples of the following: angle of linear polarization (AoLP) images, degree of linear polarization (DoLP) images, RGB images, lidar scans, ground truth free space segmentation (road segmentation), GNSS / IMU readings (vehicle location, vehicle orientation, vehicle speed, vehicle acceleration, etc.) and calibration matrices. Additionally, the dataset includes free space segmentation of 8,141 images.
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The Waymo Open Dataset currently contains 1,950 segments. The authors plan to grow this dataset in the future. Currently the datasets includes: 1,950 segments of 20s each, collected at 10Hz (390,000 frames) in diverse geographies and conditions Sensor data 1 mid-range lidar 4 short-range lidars 5 cameras (front data Lidar to camera projections Sensor calibrations and vehicle poses Labeled data Labels for 4 object classes - Vehicles, Pedestrians, Cyclists, Signs High-quality labels for lidar data in 1,200 segments 12.6M 3D bounding box labels with tracking IDs on lidar data High-quality labels for camera data in 1,000 segments 11.8M 2D bounding box labels with tracking IDs on camera data
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MapAI: Precision in Building Segmentation Dataset The dataset comprises 7500 training images and 1500 validation images from Denmark. The test dataset is split into two tasks, where the first task (1368 images) is to segment the buildings only using aerial images.
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The SemanticPOSS dataset for 3D semantic segmentation contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances.
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SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation.
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…Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction.
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…This outdoor dataset introduces falling_snow and accumulated_snow along with all the semanticKITTI classes to further AV tasks like semantic and panoptic segmentation, object detection and tracking, and
…Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities.
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…Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.
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