Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Johannes L. Schönberger"'
Autor:
Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu, the scikit-image contributors
Publikováno v:
PeerJ, Vol 2, p e453 (2014)
scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Py
Externí odkaz:
https://doaj.org/article/59ac72e7050e49868f3124aa944829e4
Autor:
Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Schönberger, Pablo Speciale, Lukas Gruber, Viktor Larsson, Ondrej Miksik, Marc Pollefeys
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200700
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ce54be70323d18880eb9601b24fdfdb
https://doi.org/10.1007/978-3-031-20071-7_40
https://doi.org/10.1007/978-3-031-20071-7_40
Publikováno v:
CVPR
Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cam
Publikováno v:
CVPR
We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space. While previous fusion methods use an explicit scene representation like signed distance functions (SDFs), we propose a learned feature re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bde78adf383e6c06b50ecf685c45e8a7
http://arxiv.org/abs/2011.14791
http://arxiv.org/abs/2011.14791
Publikováno v:
CVPR
Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b680d1d1c9b5874437356049f39bb6d
http://arxiv.org/abs/2006.06634
http://arxiv.org/abs/2006.06634
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030584511
ECCV (1)
ECCV (1)
Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy conce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb59462888db93d5c286f93cebbac175
https://doi.org/10.1007/978-3-030-58452-8_20
https://doi.org/10.1007/978-3-030-58452-8_20
Publikováno v:
CVPR
The efficient fusion of depth maps is a key part of most state-of-the-art 3D reconstruction methods. Besides requiring high accuracy, these depth fusion methods need to be scalable and real-time capable. To this end, we present a novel real-time capa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa990224c6fcaa6d44cdc2ee75b2bf43
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030584511
ECCV (1)
ECCV (1)
In this work, we address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry. Current approaches to local feature detection are inherently limited in their keypoint localization accu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b655fa9c586b9a08de8a595aaaf6cc05
https://doi.org/10.1007/978-3-030-58452-8_39
https://doi.org/10.1007/978-3-030-58452-8_39
Publikováno v:
ICCV
Augmented/mixed reality and robotic applications are increasingly relying on cloud-based localization services, which require users to upload query images to perform camera pose estimation on a server. This raises significant privacy concerns when co
Publikováno v:
Computer Vision – ECCV 2018 ISBN: 9783030012571
ECCV (12)
Computer Vision – ECCV 2018-15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part XII
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computer Vision – ECCV 2018
Lecture Notes in Computer Science, 11216
Computer Vision – ECCV 2018
ECCV (12)
Computer Vision – ECCV 2018-15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part XII
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computer Vision – ECCV 2018
Lecture Notes in Computer Science, 11216
Computer Vision – ECCV 2018
We present a novel semantic 3D reconstruction framework which embeds variational regularization into a neural network. Our network performs a fixed number of unrolled multi-scale optimization iterations with shared interaction weights. In contrast to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9eb73918cab647413254a6a1a7f6bd0
https://doi.org/10.1007/978-3-030-01258-8_20
https://doi.org/10.1007/978-3-030-01258-8_20