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Semantic 2D maps are commonly used by humans and machines for navigationpurposes, whether it's walking or driving. However, these maps havelimitations: they lack detail, often contain inaccuracies, and are difficult tocreate and maintain, especially in an automated fashion. Can we use raw imageryto automatically create better maps that can be easily interpreted by bothhumans and machines? We introduce SNAP, a deep network that learns rich neural2D maps from ground-level and overhead images. We train our model to alignneural maps estimated from different inputs, supervised only with camera posesover tens of millions of StreetView images. SNAP can resolve the location ofchallenging image queries beyond the reach of traditional methods,outperforming the state of the art in localization by a large margin. Moreover,our neural maps encode not only geometry and appearance but also high-levelsemantics, discovered without explicit supervision. This enables effectivepre-training for data-efficient semantic scene understanding, with thepotential to unlock cost-efficient creation of more detailed maps.

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding