Lending Orientation to Neural Networks for Cross-View Geo-Localization

By Liu Liu, Hongdong Li. Computer Vision and Pattern Recognition, 2019

386 citations recorded by the source corpus.

This paper studies image-based geo-localization (IBL) problem using ground-to-aerial cross-view matching. The goal is to predict the spatial location of a ground-level query image by matching it to a large geotagged aerial image database (e.g., satellite imagery). This is a challenging task due to the drastic differences in their viewpoints and visual appearances. Existing deep learning methods for this problem have been focused on maximizing feature similarity between spatially close-by image pairs, while minimizing other images pairs which are far apart. They do so by deep feature embedding based on visual appearance in those ground-and-aerial images. However, in everyday life, humans commonly use orientation information as an important cue for the task of spatial localization. Inspired by this insight, this paper proposes a novel method which endows deep neural networks with the `commonsense' of orientation. Given a ground-level spherical panoramic image as query input (and a large georeferenced satellite image database), we design a Siamese network which explicitly encodes the orientation (i.e., spherical directions) of each pixel of the images. Our method significantly boosts the discriminative power of the learned deep features, leading to a much higher recall and precision outperforming all previous methods. Our network is also more compact using only 1/5th number of parameters than a previously best-performing network. To evaluate the generalization of our method, we also created a large-scale cross-view localization benchmark containing 100K geotagged ground-aerial pairs covering a city. Our codes and datasets are available at https://github.com/Liumouliu/OriCNN.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e6fcd35992e63620600357883b92518884a6c6f1

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In Aerospace Engineering

  1. Taojiannan Yang
  2. Chen Chen
  3. Yunchao Wei
  4. Yi Yang
  5. Zhedong Zheng
  6. Sijie Zhu

In Computer Vision and Pattern Recognition

  1. Yi Yang
  2. Yunchao Wei
  3. Zhedong Zheng
  4. Chenggang Clarence Yan
  5. Luc Van Gool
  6. Liang Zheng
  7. Xiaogang Wang
  8. Chen Chen

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