Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing

• URL: http://arxiv.org/abs/2601.18252v1
• Date: Mon, 26 Jan 2026 08:16:02 GMT
• Title: Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing
• Authors: Chao Wang, Xuanying Li, Cheng Dai, Jinglei Feng, Yuxiang Luo, Yuqi Ouyang, Hao Qin,
• Abstract summary: Existing methods predict lines and junctions separately and reconcile them post-hoc, causing mismatches and reduced robustness.<n>We present Co-PLNet, a point-line collaborative framework that exchanges spatial cues between the two tasks.<n>Experiments on Wireframe and YorkUrban show consistent improvements in accuracy and robustness, together with favorable real-time efficiency.
• Score: 5.175452394220525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wireframe parsing aims to recover line segments and their junctions to form a structured geometric representation useful for downstream tasks such as Simultaneous Localization and Mapping (SLAM). Existing methods predict lines and junctions separately and reconcile them post-hoc, causing mismatches and reduced robustness. We present Co-PLNet, a point-line collaborative framework that exchanges spatial cues between the two tasks, where early detections are converted into spatial prompts via a Point-Line Prompt Encoder (PLP-Encoder), which encodes geometric attributes into compact and spatially aligned maps. A Cross-Guidance Line Decoder (CGL-Decoder) then refines predictions with sparse attention conditioned on complementary prompts, enforcing point-line consistency and efficiency. Experiments on Wireframe and YorkUrban show consistent improvements in accuracy and robustness, together with favorable real-time efficiency, demonstrating our effectiveness for structured geometry perception.

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