轻量级匹配器，高效联合点线特征匹配。
LightGlueStick: a Fast and Robust Glue for Joint Point-Line Matching
- 提出注意力线消息传递机制，显式建模线段连接关系。
- 在多个基准上达到新最优，推理速度远超现有方法。
- 适合实时系统与边缘设备部署，兼顾精度与效率。
点和线是互补的局部特征，其结合在SLAM和运动恢复结构等应用中表现优异。传统方法将点匹配与线匹配视为独立任务。近期的GlueStick提出基于图神经网络的联合匹配方法，虽降低计算复杂度但仍难以实现实时应用或边缘部署。受点匹配最新进展启发，本文提出LightGlueStick，一种轻量级点与线段匹配器。其核心创新为注意力线消息传递（ALMP），显式暴露线段连通性，促进节点间高效通信。大量实验表明，LightGlueStick在多个基准上达到新状态，代码已开源。
原文摘要 · Abstract (English)
Lines and points are complementary local features, whose combination has proven effective for applications such as SLAM and Structure-from-Motion. The backbone of these pipelines are the local feature matchers, establishing correspondences across images. Traditionally, point and line matching have been treated as independent tasks. Recently, GlueStick proposed a GNN-based network that simultaneously operates on points and lines to establish matches. While running a single joint matching reduced the overall computational complexity, the heavy architecture prevented real-time applications or deployment to edge devices. Inspired by recent progress in point matching, we propose LightGlueStick, a lightweight matcher for points and line segments. The key novel component in our architecture is the Attentional Line Message Passing (ALMP), which explicitly exposes the connectivity of the lines to the network, allowing for efficient communication between nodes. In thorough experiments we show that LightGlueStick establishes a new state-of-the-art across different benchmarks. The code is available at https://github.com/aubingazhib/LightGlueStick.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。