The paper at hand introduces a novel system for producing an enhanced semantic map that leverages a reconstruction approach of street-view scenes using computer vision and machine learning techniques. Focusing on the recognition and localization of objects/entities, the composed map combines semantic information from publicly available, yet of lower accuracy, satellite images, with more detailed data from ground-level camera measurements. This merging is achieved by utilizing odometry information from a street-moving vehicle and the 3D reconstruction of its recorded view. Then, the 3D semantic segmentation results are georeferenced and superimposed on the semantic map from the satellite images. In such a way, areas that require fine semantic accuracy can be improved, while the rest are left with the segmentation results of the satellite information. Every part of the proposed system is individually evaluated. We additionally test the overall approach on a case-study of georeferencing new labels of traffic signs, which are detected through a specifically designed classification network over a publicly available dataset collected around the city of Berlin.

本文介绍了一种新颖的系统，用于产生增强的语义图，该语义图利用计算机视觉和机器学习技术来利用街景场景的重建方法。着重于对象/实体的识别和定位，合成的地图将来自公开可用但精度较低的卫星图像的语义信息与来自地面摄像机测量的更详细的数据相结合。通过利用来自行进车辆的里程信息和其记录视图的3D重建来实现此合并。然后，对3D语义分割结果进行地理定位，并将其叠加在卫星图像的语义图上。这样，可以改善需要精确语义准确性的区域，而其余的则保留了卫星信息的分割结果。拟议系统的每个部分均经过单独评估。我们还对地理标志交通标志新标签的案例研究进行了整体测试，该案例是通过专门设计的分类网络在柏林市附近收集的公共数据集上进行检测的。