T
Tomas Pajdla
Researcher at Czech Technical University in Prague
Publications - 215
Citations - 20622
Tomas Pajdla is an academic researcher from Czech Technical University in Prague. The author has contributed to research in topics: Epipolar geometry & Structure from motion. The author has an hindex of 50, co-authored 201 publications receiving 17278 citations.
Papers
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Journal ArticleDOI
Robust wide-baseline stereo from maximally stable extremal regions
TL;DR: The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes.
Proceedings ArticleDOI
Robust wide baseline stereo from maximally stable extremal regions
TL;DR: The wide-baseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints, is studied and an efficient and practically fast detection algorithm is presented for an affinely-invariant stable subset of extremal regions, the maximally stable extremal region (MSER).
Proceedings ArticleDOI
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition
TL;DR: A convolutional neural network architecture that is trainable in an end-to-end manner directly for the place recognition task and an efficient training procedure which can be applied on very large-scale weakly labelled tasks are developed.
Proceedings ArticleDOI
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Torsten Sattler,Will Maddern,Carl Toft,Akihiko Torii,Lars Hammarstrand,Erik Stenborg,Daniel Safari,Daniel Safari,Masatoshi Okutomi,Marc Pollefeys,Marc Pollefeys,Josef Sivic,Fredrik Kahl,Fredrik Kahl,Tomas Pajdla +14 more
TL;DR: This paper introduces the first benchmark datasets specifically designed for analyzing the impact of day-night changes, weather and seasonal variations, as well as sequence-based localization approaches and the need for better local features on visual localization.
Proceedings ArticleDOI
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
Mihai Dusmanu,Ignacio Rocco,Tomas Pajdla,Marc Pollefeys,Josef Sivic,Akihiko Torii,Torsten Sattler +6 more
TL;DR: This work proposes an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector, and shows that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations.