Tasks. In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes. # Metric To assess the labeling performance, we rely on the commonly applied Jaccard Index or intersection-over-union (mIoU) metric over moving parts of the environment. 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 Exploiting Sequential Data}}, author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss}, year={2021}, journal={IEEE Robotics and Automation Letters(RA-L)}, doi = {10.1109/LRA.2021.3093567} }