统一提取点线特征，速度快10倍且内存少10倍
Unified and Efficient Point-Line Local Features
- 用同一轻量架构同时提取关键点和线段特征
- 比当前最好方法快4倍，内存占用减少10倍
- 适合实时多视图视觉任务，如三维重建
多视图计算机视觉系统通常依赖于精确的稀疏关键点和鲁棒描述子。尽管引入线特征在匹配与位姿估计中已显示出明显优势，但现有点线方法仍效率低下：关键点与线段分别检测，网络日益臃肿，且依赖耗时的CPU启发式算法，难以实现实时性能。本文提出统一高效的点线特征提取器UPAL，通过单一轻量级结构联合提取关键点、线段及特征描述子。共享主干网络为点与线特征分支提供通用表征，线段通过加速后处理阶段恢复，该阶段为LSD算法的增强高效变体。UPAL在点与线任务上均达到或超越当前最佳性能，同时显著降低计算开销，例如相比ALIKED + DeepLSD管道实现4倍加速与10倍内存压缩。代码已公开于https://github.com/francois141/upal。
原文摘要 · Abstract (English)
Multi-view computer vision pipelines typically rely on accurate sparse keypoints and robust descriptors. While incorporating line features has shown clear benefits for matching and pose estimation, existing point-line approaches remain inefficient: they detect points and lines separately, use increasingly heavy networks, and depend on CPU-bound heuristics that hinder real-time performance. We introduce a Unified Efficient Points and Lines (UPAL) feature extractor that jointly extracts keypoints, line segments, and feature descriptors within a single lightweight architecture. A shared backbone provides common representations that feed different branches for point and line features. Line segments are recovered through an accelerated post-processing stage, an enhanced and highly efficient variant of the LSD algorithm. UPAL matches or exceeds state-ofthe-art performance in both point and line applications while significantly reducing computational cost, achieving, for instance, a 4x speedup and 10x smaller memory footprint over the ALIKED + DeepLSD pipeline. Code is publicly available at https://github.com/francois141/upal.
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