no code implementations • ICCV 2023 • Emanuele Santellani, Christian Sormann, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer
In this work we introduce S-TREK, a novel local feature extractor that combines a deep keypoint detector, which is both translation and rotation equivariant by design, with a lightweight deep descriptor extractor.
no code implementations • 13 Dec 2022 • Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer
We denote our method DELS-MVS: Deep Epipolar Line Search Multi-View Stereo.
no code implementations • 10 Aug 2022 • Emanuele Santellani, Christian Sormann, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer
In order to lower the computational cost of the matching phase, we propose a deep feature extraction network capable of detecting a predefined number of complementary sets of keypoints at each image.
no code implementations • 29 Nov 2021 • Christian Sormann, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer
We present a novel deep-learning-based method for Multi-View Stereo.
Ranked #11 on Point Clouds on Tanks and Temples
no code implementations • 23 Oct 2020 • Christian Sormann, Patrick Knöbelreiter, Andreas Kuhn, Mattia Rossi, Thomas Pock, Friedrich Fraundorfer
We therefore show how we can calculate a normalization based on the expected 3D error, which we can then use to normalize the label jumps in the CRF.
no code implementations • CVPR 2020 • Mattia Rossi, Mireille El Gheche, Andreas Kuhn, Pascal Frossard
Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings.
no code implementations • 1 Dec 2019 • Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer
Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions.