  In this paper, we propose PointSeg, a real-time end-to-end semantic
segmentation method for road-objects based on spherical images. We take the
spherical image, which is transformed from the 3D LiDAR point clouds, as input
of the convolutional neural networks (CNNs) to predict the point-wise semantic
map. To make PointSeg applicable on a mobile system, we build the model based
on the light-weight network, SqueezeNet, with several improvements. It
maintains a good balance between memory cost and prediction performance. Our
model is trained on spherical images and label masks projected from the KITTI
3D object detection dataset. Experiments show that PointSeg can achieve
competitive accuracy with 90fps on a single GPU 1080ti. which makes it quite
compatible for autonomous driving applications.
