ELSD: Efficient Line Segment Detector and Descriptor

We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to simultaneously detect line segments and extract their descriptors in an image. Unlike the traditional pipelines that conduct detection and description separately, ELSD utilizes a shared feature extractor for both detection and description, to provide the essential line features to the higher-level tasks like SLAM and image matching in real time. First, we design the one-stage compact model, and propose to use the mid-point, angle and length as the minimal representation of line segment, which also guarantees the center-symmetry. The non-centerness suppression is proposed to filter out the fragmented line segments caused by lines' intersections. The fine offset prediction is designed to refine the mid-point localization. Second, the line descriptor branch is integrated with the detector branch, and the two branches are jointly trained in an end-to-end manner. In the experiments, the proposed ELSD achieves the state-of-the-art performance on the Wireframe dataset and YorkUrban dataset, in both accuracy and efficiency. The line description ability of ELSD also outperforms the previous works on the line matching task.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Line Segment Detection wireframe dataset ELSD sAP5 64.3 # 1
sAP10 68.9 # 1
sAP15 70.9 # 1
FH 83.1 # 1
Line Segment Detection York Urban Dataset ELSD sAP5 27.6 # 2
sAP10 30.2 # 2
sAP15 31.8 # 1
FH 64.8 # 3

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