Abstract
We propose a method for accurate camera pose estimation in urban environments from single images and 2D maps made of the surrounding buildings’ outlines. Our approach bridges the gap between learning-based approaches and geometric approaches: We use recent semantic segmentation techniques for extracting the buildings’ edges and the façades’ normals in the images and minimal solvers [14] to compute the camera pose accurately and robustly. We propose two such minimal solvers: one based on three correspondences of buildings’ corners from the image and the 2D map and another one based on two corner correspondences plus one façade correspondence. We show on a challenging dataset that, compared to recent state-of-the-art [1], this approach is both, faster and more accurate.
Original language | English |
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Title of host publication | Proceedings of the British Machine Vision Conference (BMVC) |
Publication status | Published - 2017 |
Event | 28th British Machine Vision Conference: BMVC 2017 - London, United Kingdom Duration: 4 Sept 2017 → 7 Apr 2018 |
Conference
Conference | 28th British Machine Vision Conference |
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Abbreviated title | BMVC 2017 |
Country/Territory | United Kingdom |
City | London |
Period | 4/09/17 → 7/04/18 |