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 at Meta's Reality Labs Research, with which I am still collaborating with.

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.

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News

  • 16th - 21st of June 2024: I'm attending CVPR 2024 in Seattle, WA, USA presenting Marigold.
  • 6th June 2024: Invited talk for the KoRaTo Team of University of Ulm at ETH Zurich.
  • 25th April 2024: Invited talk about my population mapping projects at WorldPop in Southampton, UK.
  • December 2023 - February 2024: I'm interning at Meta's Reality Labs in Redmond, WA, USA.
  • 22nd of September 2023: Invited talk at UZH Astrophysics Seminar. Recording
  • 4th - 6th of September 2023: I'm attending the Swiss Remote Sensing Days 2023 in St. Gallen, CH.
  • 15th of August 2023: Invited talk at Google's Semantic perception group.
  • 10th of August 2023: Invited talk at Google's Open Building team.
  • 1st of July 2023: Invited talk at SocialIncome to present the POMELO project.
  • 18th - 22nd of June 2023: I am attending CVPR 2023 in Vancouver, Canada presenting DADA.
  • 13th of March: Visiting the ICRC headquarters in Geneva, CH.
  • 13th of March: Invited talk about POMELO at the SDG lab workshop at Deloitte in Geneva, CH.
  • 30th of November 2022: Invited talk at ETH's Weather and Climate Risks group.
  • 14th of October 2022: Invited talk at the ETH's AI+X event , presenting POMELO.
  • 28th of June - 1st of July 2022: I'm at the Machine Learning Summer School in Krakow, Poland.
  • 23rd - 27th of May 2022: I'm attending ESA's Living Planet Symposium in Bonn, DE.
  • 1st - 4th of May 2022: I'm attending the Swiss Remote Sensing Days 2022 in Ascona, CH.
  • 1st of September 2021: I started my PhD at the Photogrammetry and Remote Sensing Lab at ETH Zurich.

Research

I'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.

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.

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.

POPCORN: High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2
Nando Metzger, Rodrigo Caye Daudt, Devis Tuia Konrad Schindler,
arXiv, 2023
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.

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

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

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.

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.

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.

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.


Thank you for the template Jon Barron.