A large fraction of acquired satellite images contain 2D projections of Earth. However, for many downstream applications, 3D understanding is beneficial or necessary. In recent years, deep learning has enabled a number of solutions for learning 3D representations from 2D satellite images.
This episode delivers an overview of some of the prominent works in this area. Mikolaj hosts 3 guests: Dawa Derksen, Roger Marí, and Yujiao Shi, providing a summary of each guest’s contributions on the topic as well as a panel discussion. Note you can also view the video of this recording on YouTube here
Dawa Derksen - Origins of Shadow-NeRF
Dawa pursued a post-doctoral research fellowship at the European Space Agency from 2020-2022, and is currently working at the Centre National d’Etudes Spatiales (French Space Agency) where he is involved in the field of 3D Implicit Representation Learning applied to Remote Sensing.
Roger Marí - EO-NeRF
Roger is a post-doc researcher from Barcelona specialised in 3D vision tasks. He is currently working at the Centre Borelli, ENS Paris-Saclay, in France, where his research topic is the application of neural rendering methods to satellite image collections. He is the author of Sat-NeRF and EO-NeRF, some of the first models in the literature to provide quantitatively convincing results in terms of surface reconstruction.
Yujiao Shi - Connecting Satellite Image with StreetView
Yujiao is a research fellow at the Australian National University. She obtained her PhD degree at the same institute. Her research interests include satellite image-based localisation, cross-view synthesis, 3D vision-related tasks, and self-supervised learning.
Host & Production: Mikolaj Czerkawski