SEG-VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data

Abstract

This paper proposes a SEG-VoxelNet that takes RGB images and LiDAR point clouds as inputs for accurately detecting 3D vehicles in autonomous driving scenarios, which for the first time introduces semantic segmentation technique to assist the 3D LiDAR point cloud based detection. Specifically, SEG-VoxelNet is composed of two sub-networks: an image semantic segmentation network (SEG-Net) and an improved-VoxelNet. The SEG-Net generates the semantic segmentation map which represents the probability of the category for each pixel. The improved-VoxelNet is capable of effectively fusing point cloud data with image semantic feature and generating accurate 3D bounding boxes of vehicles. Experiments on the KITTI 3D vehicle detection benchmark show that our approach outperforms the methods of state-of-the-art.