MPG ETH CLS Student Projects

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Efficient automated texture recognition and mapping for 3D city models

The purpose of dense 3D reconstruction is to obtain models that can be visualized on computers. Research on the topic focuses especially on improving the quality of the geometry in order to obtain models that are close to reality or making it faster in real-time.

Material/Texture Recognition Texture Mapping Segmentation 3D Models Machine Learning Computer Vision

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Simplifizierung von Dreiecksmodellen unter Berücksichtigung Geometrischer und Radiometrischer Eigenschaften

The Photogrammetry and Remote Sensing lab implemented an algorithm for the image-based reconstruction of surface meshes. Thereby an initial approximation of the surface is successively refined in a coarse-to-fine scheme.

Mesh Simplification 3D Reconstruction Airborne Images Photogrammetry Computer Vision

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Tree stress estimation with deep learning

A healthy canopy prevents soil erosion, slows rainwater runoff, and is key to clean and ample water supply. However, the amount of trees, their stress level (e.g., defoliation), species, biomass, and age are often unknown because no up-to-date database exists due to the high cost of in-situ surveys.

Satellite images deep machine learning

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Cocoa Segmentation in Satellite Images with deep learning

Cocoa is the basic ingredient to produce chocolate.For market research, big chocolate companies need to know the overall acre-age of cocoa and a rough estimation of annual yield to adjust their buying strategy accordingly.

Deep learning satellite remote sensing semantic segmentation large-scale

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Learning Control

Machine learning for advanced control of intelligent systems

Control machine learning adaptive control robotics dynamic systems optimization intelligent systems

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