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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.18252 |
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| _version_ | 1866918305350025216 |
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| author | Wang, Chao Li, Xuanying Dai, Cheng Feng, Jinglei Luo, Yuxiang Ouyang, Yuqi Qin, Hao |
| author_facet | Wang, Chao Li, Xuanying Dai, Cheng Feng, Jinglei Luo, Yuxiang Ouyang, Yuqi Qin, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18252 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing Wang, Chao Li, Xuanying Dai, Cheng Feng, Jinglei Luo, Yuxiang Ouyang, Yuqi Qin, Hao Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2601.18252 |