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[1612.00423] TorontoCity: Seeing the World with a Million Eyes

ar5iv.labs.arxiv.org · 6,584 words · saved by 1 readers

In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 𝑘 𝑚 2 of land, 8439 𝑘 𝑚 of road and around 400 , 000 buildings. Our benchmark provides different perspectives of the world captured from airplanes, drones and cars driving around the city. Manually labeling such a large scale dataset is infeasible. Instead, we propose to utilize different sources of high-precision maps to create our ground truth. Towards this goal, we develop algorithms that allow us to align all data sources with the maps while requiring minimal human supervision. We have designed a wide variety of tasks including building height estimation (reconstruction), road centerline and curb extraction, building instance segmentation, building contour extraction (reorganization), semantic labeling and scene type classification (recognition). Our pilot study shows that most of these tasks are still difficult for modern convolutional neural networks. ”It is

TorontoCity: Seeing the World with a Million Eyes Shenlong Wang, Min Bai, Gellert Mattyus, Hang Chu, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, Sanja Fidler, Raquel Urtasun Department of Computer Science, University of Toronto {slwang, mbai, mattyusg, chuhang1122, wenjie, byang, justinliang, joel, fidler, urtasun}@cs.toronto.edu Abstract In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 ​ k ​ m 2 712.5 𝑘 superscript 𝑚 2 712.5km^{2} of land, 8439 ​ k ​ m 8439 𝑘 𝑚 8439km of road and around 400 , 000 400 000 400,000 build

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