RangeNet : Fast and Accurate LiDAR Semantic Segmentation Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of highquality deep learning algorithms for imagebased recognition, highlevel semantic perception tasks are predominantly solved using highresolution cameras. As a result of that, other sensor modalities potentially useful for this task are often ignored. In this paper, we push the state of the art in LiDARonly semantic segmentation forward in order to provide another independent source of semantic information to the vehicle. Our approach can accurately perform full semantic segmentation of LiDAR point clouds at sensor frame rate. We exploit range images as an intermediate representation in combination with a Convolutional Neural Network (CNN) exploiting the rotating LiDAR sensor model. To obtain accurate results, we propose a novel postprocessing algorithm that deals with problems arising from this intermediate representation such as discretization errors and blurry CNN outputs. We implemented and thoroughly evaluated our approach including several comparisons to the state of the art. Our experiments show that our approach outperforms stateoftheart approaches, while still running online on a single embedded GPU. The code can be accessed at https:github.comPRBonnlidarbonnetal.

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.