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LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors

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Computer Vision – ECCV 2020 (ECCV 2020)

Abstract

We introduce a solution to large scale Augmented Reality for outdoor scenes by registering camera images to textured Digital Elevation Models (DEMs). To accommodate the inherent differences in appearance between real images and DEMs, we train a cross-domain feature descriptor using Structure From Motion (SFM) guided reconstructions to acquire training data. Our method runs efficiently on a mobile device and outperforms existing learned and hand-designed feature descriptors for this task.

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Notes

  1. 1.

    http://cphoto.fit.vutbr.cz/LandscapeAR/.

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Acknowledgement

This work was supported by project no. LTAIZ19004 Deep-Learning Approach to Topographical Image Analysis; by the Ministry of Education, Youth and Sports of the Czech Republic within the activity INTER-EXCELENCE (LT), subactivity INTER-ACTION (LTA), ID: SMSM2019LTAIZ. Computational resources were partly supplied by the project e-Infrastruktura CZ (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Satellite Imagery: Data provided by the European Space Agency.

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Correspondence to Jan Brejcha .

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Brejcha, J., Lukáč, M., Hold-Geoffroy, Y., Wang, O., Čadík, M. (2020). LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12374. Springer, Cham. https://doi.org/10.1007/978-3-030-58526-6_18

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