PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud In this paper, we propose PASS3D to achieve pointwise semantic segmentation for 3D point cloud. Our framework combines the efficiency of traditional geometric methods with robustness of deep learning methods, consisting of two stages: At stage 1, our accelerated cluster proposal algorithm will generate refined cluster proposals by segmenting point clouds without ground, capable of generating less redundant proposals with higher recall in an extremely short time; stage 2 we will amplify and further process these proposals by a neural network to estimate semantic label for each point and meanwhile propose a novel data augmentation method to enhance the networkxe2x80x99s recognition capability for all categories especially for nonrigid objects. Evaluated on KITTI raw dataset, PASS3D stands out against the stateoftheart on some results, making itself competent to 3D perception in autonomous driving system. Our source code will be opensourced. A video demonstration is available at https:www.youtube.comwatch?vcukEqDuP_Qw.

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.