Unified and Efficient Point-Line Local Features

Multi-view computer vision pipelines typically rely on accu-rate sparse keypoints and robust descriptors. While incorporating linefeatures has shown clear benefits for matching and pose estimation, exist-ing point-line approaches remain inefficient: they detect points and linesseparately, use increasingly heavy networks, and depend on CPU-boundheuristics that hinder real-time performance. We introduce a Unified Effi-cient Points and Lines (UPAL) feature extractor that jointly extracts key-points, line segments, and feature descriptors within a single lightweightarchitecture. A shared backbone provides common representations thatfeed different branches for point and line features. Line segments are recov-ered through an accelerated post-processing stage, an enhanced and highlyefficient variant of the LSD algorithm. UPAL matches or exceeds state-of-the-art performance in both point and line applications while significantlyreducing computational cost, achieving, for instance, a 4× speedup and10× smaller memory footprint over the ALIKED + DeepLSD pipeline.Code is publicly available at https://github.com/francois141/upal.