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About Koundinya Nouduri, Filiz Bunyak, Shizeng Yao, Hadi Aliakbarpour, Sanjeev Agarwal, Raghuveer Rao, Kannappan Palaniappan
The document proposes LMNet, a deep learning-based landmark matching pipeline for aerial images of urban scenes. LMNet uses a Siamese network with a ResNet backbone to extract features from landmark image patches. It handles multi-scale landmarks and occlusions by using a multi-patch approach. The pipeline is trained on over 0.5 million unlabeled landmark image patches generated from 3D city reconstructions using aerial imagery and camera pose data. Results show the approach enables robust landmark matching across different cities from oblique aerial views.
Detailed Information
| Author: | ['Asil Asila'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 5 |
| Language: | English |
| Format: | |
| Price: | FREE |
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