Reference Line Network: On Simultaneous Gaussian Line Detection and Connection Graph Inference
Qian Li, Rao Fu, Wen Cheng · 2024
Reference line detection is a challenging problem due to localization uncertainty and severe occlusion. To deal with the two issues, we propose a general framework for reference line detection with two modules: Gaussian line detection and connection graph inference. The first module outputs a set of Gaussian blurred lines, outlining the main compositions of the input image. For lines obscured by occlusions, the second module generates a connection graph of detected key point pairs by equidistant sampling on the feature map. Experimental results show that the proposed method could extract reference lines accurately and reliably in different application scenarios.