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Nando Metzger I am a PhD student in the Photogrammetry and Remote Sensing Lab, ETH Zürich. My supervisors are Prof. Konrad Schindler and Prof. Devis Tuia. I work on computer vision problems with various applications such as monocular depth estimation, super-resolution, and remote sensing. Moreover, I am collaborating with the ICRC to map vulnerable populations in developing countries. Previously, interned and collaborated with Meta's Reality Labs Research. I obtained my bachelor and master's degree in Geomatics Engineering from ETH Zürich. During my master's specialized in deep learning, computer vision and remote sensing. Email / Scholar / LinkedIn / Twitter / Github / Medium I am expecting to graduate in Summer/Autumn 2025. I am looking for an industry position. |
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ResearchI'm interested in computer vision, deep learning, and their applications to remote sensing. Most of my work is related to super-resolution, depth estimation or both at the same time. Some papers are highlighted. * indicates equal contribution. |
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Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, Konrad Schindler CVPR, 2024 (Oral, Best Paper Candidate) project page / arXiv / code / colab / demo Marigold is an affine-invariant monocular depth estimation method based on Stable Diffusion, leveraging its rich prior knowledge for better generalization and achieving state-of-the-art performance with significant improvements, even with synthetic training data. |
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BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
Xiang Zhang, Bingxin Ke, Hayko Riemenschneider, Nando Metzger, Anton Obukhov, Markus Gross, Konrad Schindler, Christopher Schroers arXiv, 2024 arXiv / project BetterDepth is a plug-and-play diffusion-based refiner that boosts the performance of any SOTA zero-shot monocular depth estimation method. |
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POPCORN: High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2
Nando Metzger, Rodrigo Caye Daudt, Devis Tuia Konrad Schindler Remote Sensing of Environment, 2024 project page / code / arXiv / demo / data POPCORN is a lightweight population mapping method using free satellite images and minimal data, surpassing existing accuracy and providing interpretable maps for mapping populations in data-scarce regions. |
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Thera: Neural Fields with Thermal Activations for Arbitrary-Scale Super-Resolution
Alexander Becker*, Rodrigo Caye Daudt*, Nando Metzger, Jan Dirk Wegner, Konrad Schindler arXiv, 2023 arXiv / code We present a novel approach for arbitrary-scale single image super-resolution (ASSR) that uses neural fields with an adaptive Gaussian point spread function (PSF) to prevent aliasing and achieve superior results, offering more parameter efficiency and setting a new state of the art while maintaining computational efficiency. |
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DADA: Guided Depth Super-Resolution by Deep Anisotropic Diffusion
Nando Metzger*, Rodrigo Caye Daudt*, Konrad Schindler CVPR, 2023 arXiv / paper / project page / video / poster We propose DADA, a novel approach to depth image super-resolution by combining guided anisotropic diffusion with a deep convolutional network, enhancing both edge detail and contextual reasoning. This method achieves unprecedented results in three benchmarks, especially at larger scales like x32 |
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MLFocalLengths: Estimating the Focal Length of a Single Image
Nando Metzger GitHub Project, 2023 code Information about the focal length with which a photo is taken might be obstructed (internet photos) or not available (vintage photos). Inferring the focal length of a photo solely from a monocular view is an ill-posed task that requires knowledge about the scale of objects and their distance to the camera - e.g. scene understanding. I trained a deep learning model to acquire such scene understanding to predict the focal length and open-source the model with this repository. |
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POMELO: Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Nando Metzger, John E Vargas-Muñoz, Rodrigo Caye Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Muhammad Imran, Ferda Ofli, Konrad Schindler, Devis Tuia Nature - Scientific Reports, 2022 project page / video / arXiv / community dataset POMELO is a deep learning model that creates fine-grained population maps using coarse census counts and open geodata, achieving high accuracy in sub-Saharan Africa and effectively estimating population numbers even without any census data. |
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Urban Change Forecasting from Satellite Images
Nando Metzger, Mehmet Ozgur Turkoglu Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, PFG, 2022, (Karl-Kraus Best Paper Award) paper We propose a method for forecasting the emergence and timing of new buildings using a deep neural network with a custom pretraining procedure, validated on the SpaceNet7 dataset. |
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Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
Nando Metzger*, Mehmet Ozgur Turkoglu*, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler, IEEE, TGRS, 2021 paper We propose using neural ordinary differential equations (NODEs) combined with RNNs to improve crop classification from irregularly spaced satellite images, showing enhanced accuracy over common methods, especially with few observations, and better early-season forecasting due to the continuous representation of latent dynamics. |
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DSM Refinement with Deep Encoder-Decoder Networks
Nando Metzger, Corinne Stucker, Konrad Schindler, arXiv, 2020 paper This work presents a method for automatically refining 3D city models generated from aerial images by using a neural network trained with reference data and a loss function to improve DSMs, effectively preserving geometric structures while removing noise and artifacts. |
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Thank you for the template Jon Barron. |