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Learning geometric soft constraints for multi-view instance matching across street-level panoramas

Ahmed Samy Nassar 1, 2 Nico Lang 2 Sébastien Lefèvre 1 Jan Wegner 2
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 EcoVision Lab
ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich]
Abstract : We present a new approach for matching tree instances across multiple street-view panorama images for the ultimate goal of city-scale street-tree mapping with high positioning accuracy. What makes this task challenging is the strong change in viewpoint , different lighting conditions, high similarity of neighboring trees, and variability in scale. We propose to turn (tree) instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a siamese convolutional neural network that learns to match two views of the same tree given many candidate tree image cutouts and geographic information of the two panorama images. In addition to image features, we propose utilizing location information about the camera and the tree. Our method is compared to existing patch matching methods to prove its edge over state-of-the-art. This takes us one step closer to the ultimate goal of city-wide tree mapping based solely on panorama imagery to benefit city administration.
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Submitted on : Wednesday, November 13, 2019 - 6:29:32 PM
Last modification on : Monday, November 30, 2020 - 5:28:08 PM

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Ahmed Samy Nassar, Nico Lang, Sébastien Lefèvre, Jan Wegner. Learning geometric soft constraints for multi-view instance matching across street-level panoramas. 2019 Joint Urban Remote Sensing Event (JURSE), May 2019, Vannes, France. pp.1-4, ⟨10.1109/JURSE.2019.8808935⟩. ⟨hal-02343907⟩

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