发表机构
ETH Zurich; Microsoft Spatial AI Lab（苏黎世联邦理工学院; 微软空间人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出UPAL特征提取器，联合提取点线特征，在保持最优性能的同时大幅降低计算成本，优于ALIKED+DeepLSD流程。
AI 中文摘要
多视图计算机视觉流程通常依赖准确的稀疏关键点与鲁棒描述子。尽管引入线特征已被证实对匹配与位姿估计有明确益处，但现有点线方法仍存在效率低下的问题：它们分别检测点和线，使用日益繁重的网络，且依赖CPU绑定的启发式方法，阻碍了实时性能。我们提出了统一高效点线（UPAL）特征提取器，其在单个轻量架构内联合提取关键点、线段和特征描述子。共享骨干网络提供通用表示，为点和线特征的不同分支提供输入；线段通过加速后处理阶段恢复，该阶段是LSD算法的增强且高效变体。UPAL在点和线应用中达到或超过了当前最优性能，同时显著降低了计算成本，例如，相较于ALIKED + DeepLSD流程，实现了4倍加速和10倍内存占用缩减。代码公开于此https URL。
英文摘要
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.
