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• W3146156964 abstract "Compared to feature point detection and description, detecting and matchingline segments offer additional challenges. Yet, line features represent apromising complement to points for multi-view tasks. Lines are indeedwell-defined by the image gradient, frequently appear even in poorly texturedareas and offer robust structural cues. We thus hereby introduce the firstjoint detection and description of line segments in a single deep network.Thanks to a self-supervised training, our method does not require any annotatedline labels and can therefore generalize to any dataset. Our detector offersrepeatable and accurate localization of line segments in images, departing fromthe wireframe parsing approach. Leveraging the recent progresses in descriptorlearning, our proposed line descriptor is highly discriminative, whileremaining robust to viewpoint changes and occlusions. We evaluate our approachagainst previous line detection and description methods on several multi-viewdatasets created with homographic warps as well as real-world viewpointchanges. Our full pipeline yields higher repeatability, localization accuracyand matching metrics, and thus represents a first step to bridge the gap withlearned feature points methods. Code and trained weights are available athttps://github.com/cvg/SOLD2." @default.
• W3146156964 created "2021-04-13" @default.
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• W3146156964 date "2021-04-07" @default.
• W3146156964 modified "2023-09-26" @default.
• W3146156964 title "SOLD2: Self-supervised Occlusion-aware Line Description and Detection" @default.
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