  Identifying moving objects is a crucial capability for autonomous navigation,
consistent map generation, and future trajectory prediction of objects. In this
paper, we propose a novel network that addresses the challenge of segmenting
moving objects in 3D LiDAR scans. Our approach not only predicts point-wise
moving labels but also detects instance information of main traffic
participants. Such a design helps determine which instances are actually moving
and which ones are temporarily static in the current scene. Our method exploits
a sequence of point clouds as input and quantifies them into 4D voxels. We use
4D sparse convolutions to extract motion features from the 4D voxels and inject
them into the current scan. Then, we extract spatio-temporal features from the
current scan for instance detection and feature fusion. Finally, we design an
upsample fusion module to output point-wise labels by fusing the
spatio-temporal features and predicted instance information. We evaluated our
approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better
moving object segmentation performance compared to state-of-the-art methods,
demonstrating the effectiveness of our approach in integrating instance
information for moving object segmentation. Furthermore, our method shows
superior performance on the Apollo dataset with a pre-trained model on
SemanticKITTI, indicating that our method generalizes well in different
scenes.The code and pre-trained models of our method will be released at
https://github.com/nubot-nudt/InsMOS.
