Random Walk Network for 3D Point Cloud Classification and Segmentation Object classification and segmentation via point cloud are essential for mobile robot navigation and operation. A lot of researches ranging from 3D voxels, mesh gird and multiview were proposed based on point cloud. However, an accurate point cloud classification is still a challenging problem. Inspired by multilabel classification in images and convolutional neural networks (CNN), in this paper we present a novel network, named Random Walk Network (RWNet), which directly processes raw 3D point cloud data to classify points and, as a result, segment one 3D scene. Stateoftheart methods mainly focus on the features of one point while spatial relationships are also essential in point classification. To deal with this issue, we combine both appearance features and spatial information of feature points to restrain the point cloud processing. We employ PointNet first to generate initial point labels and adopt point labels with high confidence as seeds. We then construct the similarity matrix between seeds and lowconfidencelabeled points according to their structural and spatial similarity and use Random Walk to obtain the final classification. We demonstrate our method in 3D classification task in various scenes and compare with some benchmark methods. Experimental results show that RWNet has a better performance than others.

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.