1 code implementation • CVPR 2022 • Riccardo de Lutio, Alexander Becker, Stefano D'Aronco, Stefania Russo, Jan D. Wegner, Konrad Schindler
With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source.
no code implementations • 7 Jun 2021 • Riccardo de Lutio, Yihang She, Stefano D'Aronco, Stefania Russo, Philipp Brun, Jan D. Wegner, Konrad Schindler
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts.
1 code implementation • 28 May 2021 • Riccardo de Lutio, Damon Little, Barbara Ambrose, Serge Belongie
Herbarium sheets present a unique view of the world's botanical history, evolution, and diversity.
2 code implementations • ICCV 2019 • Riccardo de Lutio, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e. g., perspective depth acquired with a time-of-flight camera) and a high-resolution guide image from a different domain (e. g., a grey-scale image from a conventional camera); and the target output is a high-resolution version of the source (in our example, a high-res depth map).