论文标题
通过图像介绍纠正故障的路线图
Correcting Faulty Road Maps by Image Inpainting
论文作者
论文摘要
由于维护道路网络是劳动力密集的，因此已经引入了许多自动道路提取方法来解决这个现实世界中的问题，这是由于丰富的大型高分辨率卫星图像以及计算机视觉中的进步所推动的。但是，它们的性能受到全面自动化现实服务中路线图提取的限制。因此，许多服务采用两步的人类在循环系统中来后处理提取的路线图：错误的路线图和自动修补道路图。我们的论文专门关注后一步，引入了一种新型的图像介绍方法，用于固定没有定制启发式方法的复杂道路几何形状的路线图，从而产生了一种容易适用于任何道路几何学提取模型的方法。我们证明了我们方法对各种现实世界道路几何形状的有效性，例如笔直和弯曲的道路，T-缝线和交叉点。
As maintaining road networks is labor-intensive, many automatic road extraction approaches have been introduced to solve this real-world problem, fueled by the abundance of large-scale high-resolution satellite imagery and advances in computer vision. However, their performance is limited for fully automating the road map extraction in real-world services. Hence, many services employ the two-step human-in-the-loop system to post-process the extracted road maps: error localization and automatic mending for faulty road maps. Our paper exclusively focuses on the latter step, introducing a novel image inpainting approach for fixing road maps with complex road geometries without custom-made heuristics, yielding a method that is readily applicable to any road geometry extraction model. We demonstrate the effectiveness of our method on various real-world road geometries, such as straight and curvy roads, T-junctions, and intersections.
