  3D semantic segmentation is one of the key tasks for autonomous driving
system. Recently, deep learning models for 3D semantic segmentation task have
been widely researched, but they usually require large amounts of training
data. However, the present datasets for 3D semantic segmentation are lack of
point-wise annotation, diversiform scenes and dynamic objects.
  In this paper, we propose the SemanticPOSS dataset, which contains 2988
various and complicated LiDAR scans with large quantity of dynamic instances.
The data is collected in Peking University and uses the same data format as
SemanticKITTI. In addition, we evaluate several typical 3D semantic
segmentation models on our SemanticPOSS dataset. Experimental results show that
SemanticPOSS can help to improve the prediction accuracy of dynamic objects as
people, car in some degree. SemanticPOSS will be published at
\url{www.poss.pku.edu.cn}.
