Compared to feature point detection and description, detecting and matching line segments offer additional challenges. Yet, line features represent a promising complement to points for multi-view tasks. Lines are indeed well-defined by the image gradient, frequently appear even in poorly textured areas and offer robust structural cues. We thus hereby introduce the first joint detection and description of line segments in a single deep network. Thanks to a self-supervised training, our method does not require any annotated line labels and can therefore generalize to any dataset. Our detector offers repeatable and accurate localization of line segments in images, departing from the wireframe parsing approach. Leveraging the recent progresses in descriptor learning, our proposed line descriptor is highly discriminative, while remaining robust to viewpoint changes and occlusions. We evaluate our approach against previous line detection and description methods on several multi-view datasets created with homographic warps as well as real-world viewpoint changes. Our full pipeline yields higher repeatability, localization accuracy and matching metrics, and thus represents a first step to bridge the gap with learned feature points methods. Code and trained weights are available at https://github.com/cvg/SOLD2.
翻译:与特征点探测和描述、探测和匹配线段相比,线段部分将带来更多的挑战。但是,线段特征是多视图任务各点的一个很有希望的补充。线条确实由图像梯度来很好地界定,甚至经常出现在不完善的纹理区域,并提供了强有力的结构提示。因此,我们在此推出对单一深度网络线段的首次联合探测和描述。通过自我监督的培训,我们的方法不需要任何附加注释的线条标签,因此可以概括到任何数据集中。我们的探测器提供了从线条截断方法出发的图像线段的可重复性和准确的本地化。利用描述器学习的最新进展,我们提议的线条描述器非常具有高度的差别性,同时仍然能够查看变化和封闭性。我们对照以前在多个多视图数据集上通过人口图谱扭曲器和真实世界视角变化产生的线条探测和描述方法评估了我们的方法。我们的全部管道产生更高的重复性、本地化精度和匹配度,从而代表了缩小与学习的地貌点差距的第一步。在 httpscodeb/commds。