arXiv:2601.18252v2 Announce Type: replace-cross 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), whic
The continuous advancements in computer vision and deep learning allow for increasingly sophisticated approaches to fundamental tasks like wireframe parsing, enabling robust solutions to long-standing challenges.
Improved wireframe parsing directly impacts the robustness and accuracy of downstream applications such as robotic navigation, augmented reality, and 3D reconstruction, accelerating progress in fields reliant on geometric understanding.
This collaborative approach between point and line detection could lead to more robust and accurate geometric representations from visual data, reducing errors and improving the reliability of systems dependent on such parsing.
- · Robotics companies
- · SLAM developers
- · Computer Vision researchers
- · Augmented Reality platforms
- · Companies with less sophisticated geometric parsing solutions
More reliable understanding of physical environments for AI systems.
Faster development and deployment of autonomous systems in complex, unstructured environments.
Enhanced automation in manufacturing, logistics, and exploration leading to productivity gains and new applications.
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Read at arXiv cs.AI