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This paper introduces VolMap, a real-time approach for the semanticsegmentation of a 3D LiDAR surrounding view system in autonomous vehicles. Wedesigned an optimized deep convolution neural network that can accuratelysegment the point cloud produced by a 360\degree{} LiDAR setup, where the inputconsists of a volumetric bird-eye view with LiDAR height layers used as inputchannels. We further investigated the usage of multi-LiDAR setup and its effecton the performance of the semantic segmentation task. Our evaluations arecarried out on a large scale 3D object detection benchmark containing a LiDARcocoon setup, along with KITTI dataset, where the per-point segmentation labelsare derived from 3D bounding boxes. We show that VolMap achieved an excellentbalance between high accuracy and real-time running on CPU.

VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view