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We propose LU-Net -- for LiDAR U-Net, a new method for the semanticsegmentation of a 3D LiDAR point cloud. Instead of applying some global 3Dsegmentation method such as PointNet, we propose an end-to-end architecture forLiDAR point cloud semantic segmentation that efficiently solves the problem asan image processing problem. We first extract high-level 3D features for eachpoint given its 3D neighbors. Then, these features are projected into a 2Dmultichannel range-image by considering the topology of the sensor. Thanks tothese learned features and this projection, we can finally perform thesegmentation using a simple U-Net segmentation network, which performs verywell while being very efficient. In this way, we can exploit both the 3D natureof the data and the specificity of the LiDAR sensor. This approach outperformsthe state-of-the-art by a large margin on the KITTI dataset, as our experimentsshow. Moreover, this approach operates at 24fps on a single GPU. This is abovethe acquisition rate of common LiDAR sensors which makes it suitable forreal-time applications.

LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net