  This paper introduces VolMap, a real-time approach for the semantic
segmentation of a 3D LiDAR surrounding view system in autonomous vehicles. We
designed an optimized deep convolution neural network that can accurately
segment the point cloud produced by a 360\degree{} LiDAR setup, where the input
consists of a volumetric bird-eye view with LiDAR height layers used as input
channels. We further investigated the usage of multi-LiDAR setup and its effect
on the performance of the semantic segmentation task. Our evaluations are
carried out on a large scale 3D object detection benchmark containing a LiDAR
cocoon setup, along with KITTI dataset, where the per-point segmentation labels
are derived from 3D bounding boxes. We show that VolMap achieved an excellent
balance between high accuracy and real-time running on CPU.
