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. Our code is available at https://github.com/GalacticHogrider/Co-PLNet.

翻译：线框解析旨在恢复线段及其连接点，以形成结构化的几何表示，从而服务于同步定位与地图构建（SLAM）等下游任务。现有方法分别预测线段和连接点，并在事后进行协调，导致匹配错误且鲁棒性下降。我们提出Co-PLNet，一种点线协作框架，通过在两个任务间交换空间线索，其中早期检测结果通过点线提示编码器（PLP-Encoder）转换为空间提示，该编码器将几何属性编码为紧凑且空间对齐的特征图。跨引导线段解码器（CGL-Decoder）随后利用基于互补提示的稀疏注意力机制优化预测结果，强制实现点线一致性并提升效率。在Wireframe和YorkUrban数据集上的实验显示，该方法在精度和鲁棒性上取得持续改进，同时兼具良好的实时效率，证明了其在结构化几何感知任务中的有效性。我们的代码开源在 https://github.com/GalacticHogrider/Co-PLNet。