Instance segmentation of lidar point clouds
ICRA(2020)
摘要
We propose a robust baseline method for instance segmentation which are specially designed for largescale 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 and the source code will be published at https://github. com/feihuzhang/LiDARSeg.
更多查看译文
关键词
single-shot instance prediction,LiDAR instance segmentation,LiDAR point cloud dataset,point-wise labels,robust baseline method,large-scale outdoor LiDAR point clouds,dense feature encoding technique,precise 3D bounding box
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络