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
this https URL

本文提出了第一种单一深度神经网络同时进行线段检测和描述。通过自我监控训练，我们的方法不需要任何注释的线标签，并且可以推广到任何数据集。在多视图数据集及实际应用中，本方法相较以往的线条检测与描述算法拥有更高的稳定性和匹配度，是向学习特征点方法迈出的第一步。