Semantic Segmentation and Scene Reconstruction for Traffic Simulation Using CNN

In this paper, we propose a framework for 3D traffic scenes construction based on semantic segmentation using convolutional neural networks (CNNs). Firstly, the segmentation network, whose architecture is constructed with encoder-decoder model and a hall module, divides traffic scenes into different parts: Road, sky, vehicle and other regions. Furthermore, we generate spatio-temporal graph models and construct 3D traffic scenes according to semantic segmentation results. The applications for scene simulation are then developed. The experimental results on the KITTI dataset demonstrate the effectiveness of the proposed framework.