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In this paper, we propose PointSeg, a real-time end-to-end semanticsegmentation method for road-objects based on spherical images. We take thespherical image, which is transformed from the 3D LiDAR point clouds, as inputof the convolutional neural networks (CNNs) to predict the point-wise semanticmap. To make PointSeg applicable on a mobile system, we build the model basedon the light-weight network, SqueezeNet, with several improvements. Itmaintains a good balance between memory cost and prediction performance. Ourmodel is trained on spherical images and label masks projected from the KITTI3D object detection dataset. Experiments show that PointSeg can achievecompetitive accuracy with 90fps on a single GPU 1080ti. which makes it quitecompatible for autonomous driving applications.

PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud