Instance segmentation of lidar point clouds

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We propose a robust baseline method for instance segmentation which are specially designed for large-scale outdoor LiDAR point clouds. Our method includes a novel dense feature encoding technique, allowing the localization and segmentation of small, far-away objects, a simple but effective solution for single-shot instance prediction and effective strategies for handling severe class imbalances. Since there is no public dataset for the study of LiDAR instance segmentation, we also build a new publicly available LiDAR point cloud dataset to include both precise 3D bounding box and point-wise labels for instance segmentation, while still being about 3~20 times as large as other existing LiDAR datasets. The dataset will be published at https://github.com/feihuzhang/LiDARSeg.

Recommended citation: Zhang, Feihu, Chenye Guan, Jin Fang, Song Bai, Ruigang Yang, Philip HS Torr, and Victor Prisacariu. “Instance segmentation of lidar point clouds.” In 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9448-9455. IEEE, 2020.