Contrast-Guided Wireframe Parsing

Abstract

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Existing deep wireframe parsing methods typically focus on the semantic saliency of scene structural lines without paying particular attention to their visual saliency. As a result, these methods often face the challenge of multiple responses to proximate line segments or erroneous responses to non-structural elements like shadows. To address this fundamental issue, a novel Contrast Guidance Module (CGM) is proposed. In the CGM, a low-level image attribute, i.e., the image contrast, is leveraged to measure the visual saliency of structural lines. The CGM augments feature maps in CNN networks, subtly balancing the interpretation of line segments with their contextual significance in the image. This approach not only refines detection accuracy but also enriches the understanding of spatial geometry. Extensive experiments conducted on benchmark datasets have shown that our proposed CGM consistently outperforms the current state-of-the-art methods.