SKYLINE2GPS: Localization in urban canyons using omni-skylines
By Srikumar Ramalingam, Sofien Bouaziz, Peter F. Sturm, Matthew Brand. 2010
89 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Environmental Engineering, Remote Sensing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Eric Müller-Budack
- Kader Pustu-Iren
- Ralph Ewerth
- Tobias Weyand
- Martial Hebert
- Ilya Kostrikov
- James Philbin
- Jason Lin
In Computer Vision and Pattern Recognition
- Ming-Ming Cheng
- Luc Van Gool
- Ling Shao
- Jitendra Malik
- Ming-Hsuan Yang
- Dacheng Tao
- Abhinav Kumar Gupta
- Xiaogang Wang
In Environmental Engineering
In Remote Sensing
Citations in the corpus
- PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, methodology): “Skyline2GPS [41] matches the skyline captured by an upwardfacing camera against a 3D model of the city.”
- 3DNN: 3D Nearest Neighbor (2014, background): “In the area of outdoor scene understanding, prior work (Baboud et al. 2011; Baatz et al. 2012; Ramalingam et al. 2010) investigated matching images with models of terrain or cities.”
- Semantic Edge Detection with Diverse Deep Supervision (2018, background): “…wide-ranging applications, including object proposal generation (Bertasius et al. 2015b), occlusion and depth reasoning (Amer et al. 2015; Bian et al. 2021), 3D reconstruction (Shan et al. 2014), object detection (Ferrari et al. 2008, 2010), and image-based localization (Ramalingam et al. 2010).”
- Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, background): “Other proposals match query images against 3D models of cities [10,19,24,27,30].”