This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cloud is turned into a 2D range-image by exploiting the topology of the sensor. This image is then used as input to a U-net. This architecture has already proved its efficiency for the task of semantic segmentation of medical images. We propose to demonstrate how it can also be used for the accurate semantic segmentation of a 3D LiDAR point cloud. Our model is trained on range-images built from KITTI 3D object detection dataset. Experiments show that RIU-Net, despite being very simple, outperforms the state-of-the-art of range-image based methods. Finally, we demonstrate that this architecture is able to operate at 90fps on a single GPU, which enables deployment on low computational power systems such as robots.
翻译:本文提议了 RIU- Net (用于范围图像 U- Net), 用于3D LiDAR 点云的语义分割的流行语义分割网 。 点云通过利用传感器的地形学转换成 2D 范围映射。 此图像随后用作U- net 的输入。 此结构已证明它对于医疗图像的语义分割任务来说是有效的。 我们提议演示它如何能够用于3D LiDAR 点云的准确语义分割。 我们的模型受过KITTI 3D 对象探测数据集所建立的范围映射的培训。 实验显示, RIU- Net尽管非常简单, 却超越了基于范围映射方法的状态。 最后, 我们证明这一结构能够在一个单一的GPU上以90英尺的速度运行, 从而能够将像机器人这样的低计算能力系统部署。