VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view
This addresses the need for efficient perception in autonomous driving, but it is incremental as it builds on existing methods with optimizations.
The paper tackles real-time semantic segmentation of 3D LiDAR data for autonomous vehicles, achieving a balance between high accuracy and real-time performance on CPU.
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.