Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing
This addresses wireframe parsing for structured geometry perception in applications like SLAM, with incremental improvements over existing methods.
The paper tackles the problem of wireframe parsing, where existing methods suffer from mismatches between separately predicted lines and junctions, by introducing Co-PLNet, a collaborative point-line network that exchanges spatial cues between tasks. The result shows consistent improvements in accuracy and robustness on Wireframe and YorkUrban datasets, with favorable real-time efficiency.
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.