3 code implementations • 4 Dec 2023 • Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler
Monocular depth estimation is a fundamental computer vision task.
Ranked #6 on Monocular Depth Estimation on NYU-Depth V2 (using extra training data)
1 code implementation • 29 Nov 2023 • Alexander Becker, Rodrigo Caye Daudt, Nando Metzger, Jan Dirk Wegner, Konrad Schindler
We present a novel way to design neural fields such that points can be queried with an adaptive Gaussian PSF, so as to guarantee correct anti-aliasing at any desired output resolution.
no code implementations • 23 Nov 2023 • Nando Metzger, Rodrigo Caye Daudt, Devis Tuia, Konrad Schindler
With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns.
1 code implementation • CVPR 2023 • Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler
In this work, we propose a novel approach which combines guided anisotropic diffusion with a deep convolutional network and advances the state of the art for guided depth super-resolution.
1 code implementation • 8 Nov 2022 • Nando Metzger, John E. Vargas-Muñoz, Rodrigo C. Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Ferda Ofli, Muhammad Imran, Konrad Schindler, Devis Tuia
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
no code implementations • 27 Apr 2022 • Nando Metzger, Mehmet Özgür Türkoglu, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler
In Stage 1, a U-Net backbone is pretrained within a Siamese network architecture that aims to solve a (building) change detection task.
no code implementations • 14 Dec 2020 • Nando Metzger
However, the calculated DSMs suffer from noise, artefacts, and data holes that have to be manually cleaned up in a time-consuming process.
1 code implementation • 4 Dec 2020 • Nando Metzger, Mehmet Ozgur Turkoglu, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.