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pro vyhledávání: '"Christian Sormann"'
We propose a novel approach for deep learning-based Multi-View Stereo (MVS). For each pixel in the reference image, our method leverages a deep architecture to search for the corresponding point in the source image directly along the corresponding ep
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64aee9051c68a871c0dea805577ab5f8
Autor:
Christian Sormann, Friedrich Fraundorfer, Patrick Knöbelreiter, Alexander Shekhovtsov, Thomas Pock
Publikováno v:
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR
CVPR
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrep
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be5e4b0aa5fd2f0541980c81fb340d84
Autor:
Friedrich Fraundorfer, Mattia Rossi, Thomas Pock, Andreas Kuhn, Patrick Knöbelreiter, Christian Sormann
Publikováno v:
3DV
In this work, we propose BP-MVSNet, a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a differentiable Conditional Random Field (CRF) layer for regularization. To this end, we propose to extend the BP layer and add w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f72ceb8186a4d328530641e7016e24b
Publikováno v:
3DV
Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions. However, the use of DNNs in the context of 3D reconstruction from large and high-resolution image datasets is still an open challenge, due to mem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e34ea8e0a311278dec4332f14286be8
https://infoscience.epfl.ch/record/286978
https://infoscience.epfl.ch/record/286978