论文标题
rangeseg：3D激光点云的范围感知实时分段
RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds
论文作者
论文摘要
基于3D激光雷达点云的语义户外场景理解是由于稀疏和不规则的数据结构而用于自动驾驶的一项艰巨任务。本文占据了不同LIDAR激光束的不均范围分布的优势，以提出一个范围的意识实例分割网络Rangeseg。 Rangeseg使用具有两个依赖性解码器的共享编码器主链。重型解码器仅计算范围图像的顶部，其中远处和小物体定位以提高小物体检测精度，而光解码器计算整个范围图像，以低计算成本。结果通过DBSCAN方法进一步聚集，并具有分辨率加权距离函数，以获得实例级分割结果。 KITTI数据集的实验显示，Rangeseg的表现优于最新的语义分割方法，并提高了小型和远处的实例级分割性能。整个Rangeseg管道都满足NVIDIA \ TextSuperScript {\ textregistered} Jetson Agx Xavier的实时要求，平均每秒19帧。
Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. RangeSeg uses a shared encoder backbone with two range dependent decoders. A heavy decoder only computes top of a range image where the far and small objects locate to improve small object detection accuracy, and a light decoder computes whole range image for low computational cost. The results are further clustered by the DBSCAN method with a resolution weighted distance function to get instance-level segmentation results. Experiments on the KITTI dataset show that RangeSeg outperforms the state-of-the-art semantic segmentation methods with enormous speedup and improves the instance-level segmentation performance on small and far objects. The whole RangeSeg pipeline meets the real time requirement on NVIDIA\textsuperscript{\textregistered} JETSON AGX Xavier with 19 frames per second in average.
