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Image Matching Across Wide Baselines: From Paper to Practice

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Abstract

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task—the accuracy of the reconstructed camera pose—as our primary metric. Our pipeline’s modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of structure from motion pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online (https://github.com/ubc-vision/image-matching-benchmark), providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge (https://image-matching-challenge.github.io).

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Notes

  1. In (Barroso-Laguna et al. 2019) the models are converted to TensorFlow—we use the original PyTorch version.

  2. https://github.com/danini/graph-cut-ransac/tree/benchmark-version.

  3. https://github.com/ubc-vision/image-matching-benchmark-baselines.

  4. https://github.com/etrulls/slurm-gcp.

  5. Time measured on ‘1-standard-2’ VMs on Google Cloud Compute: 2 vCPUs with 7.5 GB of RAM and no GPU.

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Authors and Affiliations

  1. The University of British Columbia, Vancouver, BC, Canada

    Yuhe Jin & Kwang Moo Yi

  2. Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia

    Dmytro Mishkin & Jiri Matas

  3. Computer Vision Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Anastasiia Mishchuk & Pascal Fua

  4. Google Research, Zurich, Switzerland

    Eduard Trulls

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This work was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant “Deep Visual Geometry Machines” (RGPIN-2018-03788), by systems supplied by Compute Canada, and by Google’s Visual Positioning Service. DM and JM were supported by OP VVV funded Project CZ.02.1.01/0.0/0.0/16 019/0000765 “Research Center for Informatics”. DM was also supported by CTU student Grant SGS17/185/OHK3/3T/13 and by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry for Digital and Economic Affairs, and the Province of Upper Austria in the frame of the COMET center SCCH. AM was supported by the Swiss National Science Foundation.

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Jin, Y., Mishkin, D., Mishchuk, A. et al. Image Matching Across Wide Baselines: From Paper to Practice. Int J Comput Vis 129, 517–547 (2021). https://doi.org/10.1007/s11263-020-01385-0

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