We address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, pedestrians and cyclists. We formulate this problem as a point-wise classification problem, and propose an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map, which is then refined by a conditional random field (CRF) implemented as a recurrent layer. Instance-level labels are then obtained by conventional clustering algorithms. Our CNN model is trained on LiDAR point clouds from the KITTI [1] dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI. To obtain extra training data, we built a LiDAR simulator into Grand Theft Auto $\boldsymbol{V}$ (GTA-V), a popular video game, to synthesize large amounts of realistic training data. Our experiments show that SqueezeSeg achieves high accuracy with astonishingly fast and stable runtime ($8.7\pm 0.5$ ms per frame), highly desirable for autonomous driving. Furthermore, additionally training on synthesized data boosts validation accuracy on real-world data. Our source code is open-source released111https://github.com/BichenWuUCB/SqueezeSeg. The paper is accompanied by a video222https://youtu.be/Xyn5Zd31m6s containing a high level introduction and demonstrations of this work.