MARS - A Foundational Map Auto-Regressor

Map generation tasks, featured by extensive non-structural vectorized data (e.g., points, polylines, and polygons), pose significant challenges to common pixel-wise generative models. Past works, by segmenting and then performing various vectorized post-processing, usually sacrifice accuracy. Motivated by the recent huge success of auto-regressive visual-language modeling, we propose the first map foundational model: Map Auto-Regressor (MARS), that is capable of generating both multi-polyline road networks and polygon buildings in a unified manner. We collected by far the largest multi-class map dataset, MAP-3M, to support the robust training. Extensive benchmarks highlight the superiority of MARS against literature works. Meanwhile, benefited from the auto-regressive and teaching-forcing based training, we develop the “Chat with MARS” capability that enables interactive human-in-the-loop map generation and correction.