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

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12353)

Abstract

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

Keywords

Vectorization Remote sensing Deep learning Planar graph 

Notes

Acknowledgement

This research is partially supported by NSERC Discovery Grants, NSERC Discovery Grants Accelerator Supplements, and DND/NSERC Discovery Grant Supplement. This research is also supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DOI/IBC) contract number D17PC00288. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI/IBC, or the U.S. Government.

Supplementary material

504445_1_En_42_MOESM1_ESM.pdf (16.9 mb)
Supplementary material 1 (pdf 17335 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Simon Fraser UniversityBurnabyCanada

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