仅用事件相机实现实时线段检测与追踪，无需额外摄像头。
Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera
- 采用网格分配架构，通过速度无关的事件表示提升鲁棒性。
- 在高事件率下实现毫秒级响应，精度优于现有纯事件方法。
- 适合自动驾驶、机器人等需要轻量化实时感知的场景。
线段提取能有效捕捉人造环境中的几何特征。事件相机通过异步响应边缘对比变化，可减少冗余数据，实现高效提取。然而，现有方法常依赖额外帧相机，或难以应对高事件率。本文提出仅使用现代高分辨率（即高事件率）事件相机的实时线段检测与追踪方法。所提网格分配流水线包含：(i) 速度无关的事件表示，(ii) 基于拟合评分的线段检测，(iii) 通过端点扰动实现线段追踪。在自建数据集及公开数据集上的评估表明，该方法具备实时性能，且精度优于当前最优的纯事件与事件-帧混合基线，可在真实场景中实现完全独立的事件相机运行。
原文摘要 · Abstract (English)
Line segment extraction is effective for capturing geometric features of human-made environments. Event-based cameras, which asynchronously respond to contrast changes along edges, enable efficient extraction by reducing redundant data. However, recent methods often rely on additional frame cameras or struggle with high event rates. This research addresses real-time line segment detection and tracking using only a modern, high-resolution (i.e., high event rate) event-based camera. Our lattice-allocated pipeline consists of (i) velocity-invariant event representation, (ii) line segment detection based on a fitting score, (iii) and line segment tracking by perturbating endpoints. Evaluation using ad-hoc recorded dataset and public datasets demonstrates real-time performance and higher accuracy compared to state-of-the-art event-only and event-frame hybrid baselines, enabling fully stand-alone event camera operation in real-world settings.
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