Title: | StreetGAN: towards road network synthesis with generative adversarial networks |
Authors: | Hartmann, Stefan Weinmann, Michael Wessel, Raoul Klein, Reinhard |
Citation: | WSCG 2017: full papers proceedings: 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 133-142. |
Issue Date: | 2017 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | wscg.zcu.cz/WSCG2017/!!_CSRN-2701.pdf http://hdl.handle.net/11025/29554 |
ISBN: | 978-80-86943-44-2 |
ISSN: | 2464–4617 (print) 2464–4625 (CD-ROM) |
Keywords: | hluboké učení;generativní modelování;generativní nepřátelské sítě;generování silniční sítě |
Keywords in different language: | deep learning;generative modeling;generative adversarial networks;road network generation |
Abstract: | We propose a novel example-based approach for road network synthesis relying on Generative Adversarial Networks (GANs), a recently introduced deep learning technique. In a pre-processing step, we first convert a given representation of a road network patch into a binary image where pixel intensities encode the presence or absence of streets. We then train a GAN that is able to automatically synthesize a multitude of arbitrary sized street networks that faithfully reproduce the style of the original patch. In a post-processing step, we extract a graph-based representation from the generated images. In contrast to other methods, our approach does neither require domainspecific expert knowledge, nor is it restricted to a limited number of street network templates. We demonstrate the general feasibility of our approach by synthesizing street networks of largely varying style and evaluate the results in terms of visual similarity as well as statistical similarity based on road network similarity measures. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG 2017: Full Papers Proceedings |
Files in This Item:
File | Description | Size | Format | |
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Hartmann.pdf | Plný text | 6,1 MB | Adobe PDF | View/Open |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/29554
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