تقرير
Correcting Faulty Road Maps by Image Inpainting
| العنوان: | Correcting Faulty Road Maps by Image Inpainting |
|---|---|
| المؤلفون: | Hong, Soojung, Choi, Kwanghee |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted to ICASSP 2024. Implementation available at https://github.com/SoojungHong/image_inpainting_model_for_lane_geomery_discovery |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.06544 |
| رقم الأكسشن: | edsarx.2211.06544 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |