From Coarse to Fine: Robust Hierarchical Localization at Large Scale
| Autor: | Cesar Cadena, Paul-Edouard Sarlin, Marcin Dymczyk, Roland Siegwart |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning Computer Science - Computer Vision and Pattern Recognition Robotics 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Coarse to fine Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Augmented reality Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | CVPR |
| Popis: | Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes. State-of-the-art methods not only struggle with such scenarios, but are often too resource intensive for certain real-time applications. In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization. We exploit the coarse-to-fine localization paradigm: we first perform a global retrieval to obtain location hypotheses and only later match local features within those candidate places. This hierarchical approach incurs significant runtime savings and makes our system suitable for real-time operation. By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale localization. Camera-ready for CVPR 2019 |
| Databáze: | OpenAIRE |
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