协同点线检测提升线稿解析准确率与实时性
Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing
- 点线任务早期交互，用空间提示引导联合推理
- 在Wireframe和YorkUrban上精度显著提升，支持实时运行
- 适合需要结构化几何感知的视觉任务应用
线稿解析旨在恢复线段及其连接点，形成可用于同步定位与建图（SLAM）等下游任务的结构化几何表示。现有方法分别预测线段与连接点，再后期融合，易产生不匹配且鲁棒性差。本文提出Co-PLNet，一种点线协同框架，通过点线提示编码器（PLP-Encoder）将早期检测转化为紧凑的空间提示图，编码几何属性；再由交叉引导线解码器（CGL-Decoder）利用稀疏注意力机制，基于互补提示精修预测，强制点线一致性并提升效率。在Wireframe和YorkUrban数据集上的实验表明，该方法在准确率和鲁棒性上均有持续提升，并具备良好的实时性能，验证了其在结构化几何感知中的有效性。代码已开源。
原文摘要 · Abstract (English)
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.
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