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This paper tackles a 2D architecture vectorization problem, whose task is toinfer an outdoor building architecture as a 2D planar graph from a single RGBimage. We provide a new benchmark with ground-truth annotations for 2,001complex buildings across the cities of Atlanta, Paris, and Las Vegas. We alsopropose a novel algorithm utilizing 1) convolutional neural networks (CNNs)that detects geometric primitives and infers their relationships and 2) aninteger programming (IP) that assembles the information into a 2D planar graph.While being a trivial task for human vision, the inference of a graph structurewith an arbitrary topology is still an open problem for computer vision.Qualitative and quantitative evaluations demonstrate that our algorithm makessignificant improvements over the current state-of-the-art, towards anintelligent system at the level of human perception. We will share code anddata.

Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Inference