Gluestick: Robust image matching by sticking points and lines together
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Date
2023
Publication Type
Conference Paper
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yes
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Abstract
Line segments are powerful features complementary to points. They offer structural cues, robust to drastic viewpoint and illumination changes, and can be present even in texture-less areas. However, describing and matching them is more challenging compared to points due to partial occlusions, lack of texture, or repetitiveness. This paper introduces a new matching paradigm, where points, lines, and their descriptors are unified into a single wireframe structure. We propose GlueStick, a deep matching Graph Neural Network (GNN) that takes two wireframes from different images and leverages the connectivity information between nodes to better glue them together. In addition to the increased efficiency brought by the joint matching, we also demonstrate a large boost of performance when leveraging the complementary nature of these two features in a single architecture. We show that our matching strategy outperforms the state-of-the-art approaches independently matching line segments and points for a wide variety of datasets and tasks. Code is available at https://github.com/cvg/GlueStick.
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published
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2023 IEEE/CVF International Conference on Computer Vision (ICCV)
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Pages / Article No.
9672 - 9672
Publisher
IEEE
Event
19th IEEE/CVF International Conference on Computer Vision (ICCV 2023)
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03766 - Pollefeys, Marc / Pollefeys, Marc