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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 2/2021

07.10.2020

Image Matching Across Wide Baselines: From Paper to Practice

verfasst von: Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, Eduard Trulls

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2021

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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).
Vorheriger Artikel Binarized Neural Architecture Search for Efficient Object Recognition
Nächster Artikel HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

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Fußnoten
1
In (Barroso-Laguna et al. 2019) the models are converted to TensorFlow—we use the original PyTorch version.
 
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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Metadaten
Titel
Image Matching Across Wide Baselines: From Paper to Practice
verfasst von
Yuhe Jin
Dmytro Mishkin
Anastasiia Mishchuk
Jiri Matas
Pascal Fua
Kwang Moo Yi
Eduard Trulls
Publikationsdatum
07.10.2020
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 2/2021
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-020-01385-0

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