Unsupervised Segmentation Of Urban 3D Point Cloud Based On Lidarimage As one of the most vital fields in 3D scene understanding, segmentation of lidar point cloud has verity of applications such as unmanned vehicle. In order to deal with the problem of limited local computing resources, this paper proposes a twostep unsupervised learningfree method to segment sparse 3D point cloud with low computational demands. The 3D point cloud data is projected into a lidarspecific 2D coordinate system called lidarimage. For unmanned vehicles, road is a special kind of object, so the algorithm divides road firstly by using improved lidarhistogram. The algorithm further examines the geometric connection between adjacent points in 3D space, thus dividing nonroad points into independent subsets. Experiments on KITTI dataset validate the proposed algorithm outperforms similar methods.

LUNet: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on EndtoEndLearned 3D Features and UNet We propose LUNetfor LiDAR UNet, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet, we propose an endtoend architecture for LiDAR point cloud semantic segmentation that efficiently solves the problem as an image processing problem. We first extract highlevel 3D features for each point given its 3D neighbors. Then, these features are projected into a 2D multichannel rangeimage by considering the topology of the sensor. Thanks to these learned features and this projection, we can finally perform the segmentation using a simple UNet segmentation network, which performs very well while being very efficient. In this way, we can exploit both the 3D nature of the data and the specificity of the LiDAR sensor. This approach outperforms the stateoftheart by a large margin on the KITTI dataset, as our experiments show. Moreover, this approach operates at 24fps on a single GPU. This is above the acquisition rate of common LiDAR sensors which makes it suitable for realtime applications.