HF-Net: Robust Hierarchical Localization at Large Scale

We introduce a 6-DoF visual localization method that is accurate, scalable, and efficient, using HF-Net, a monolithic deep neural network for descriptor extraction. The proposed solution achieves state-of-the-art accuracy on several large-scale public benchmarks while running in real-time.

arXiv paper link

Download SfM Models and Trained Weights

Navigate to http://robotics.ethz.ch/~asl-datasets/2019_CVPR_hierarchical_localization/ for the list of all files available for download or select specific items in the list below.

SfM Models

SfM models built with SuperPoint, and usable with HF-Net, for the following datasets: Aachen Day-Night, CMU Seasons, Extended CMU Seasons and RobotCar Seasons.

Aachen Day-Night

CMU Seasons

Extended CMU Seasons

RobotCar Seasons

Trained Weights and TF Graph for HF-Net

The Tensorflow graph and trained weights.

HF-Net Tensorflow

GitHub Repository

Apart from this collection of SfM models and the trained Tensorflow graph, we also share our source code to train the network, perform inference and full 6-DoF pose retrieval.

Go to Github

The soures in https://github.com/ethz-asl/hfnet allow you to:

  • Perform state-of-the-art 6-DoF hierarchical localization using a flexible Python pipeline
  • Train HF-Net with multi-task distillation in TensorFlow
  • Evaluate feature detectors and descriptors on standard benchmarks
  • Build Structure-from-Motion models based on state-of-the-art learned features


Please consider citing the corresponding publication if you use this work in an academic context:

  title={From Coarse to Fine: Robust Hierarchical Localization at Large Scale},
  author={Sarlin, P.-E. and Cadena, C. and Siegwart, R. and Dymczyk, M.},
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