AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels

Neural map matchers estimate an image’s 3-DoF pose rela-tive to a 2D map. These models are trained on large-scale datasets ofgeo-referenced images, whose position and heading labels often containnoise that affects the trained models. To address this, we present Auto-Compass, a supervision approach for training neural map matchers frominaccurate absolute pose labels. First, we show that heading labels areunnecessary: trained from raw GPS labels, models learn to predict accu-rate headings, automatically. Second, defining a tolerance region aroundraw GPS improves positional accuracy. Third, if available, our supervi-sion uses relative poses between training images, obtained via SLAM orSfM, which provide a more accurate training signal. Across driving andegocentric benchmarks, AutoCompass consistently outperforms counter-parts trained with the usual strong reliance on absolute pose labels.