Computer Vision and Geometry GroupOpen OpportunitiesThe goal of this project is to use language prompts to help find object parts in 3D. - Computer Vision
- Master Thesis, Semester Project
| The objective of this project is to determine the metric relative pose between two images using object-to-object matches. - Computer Vision
- Master Thesis, Semester Project
| We extend the lamar.ethz.ch benchmark to develop accurate SLAM methods that can co-register drones, legged robots, wheeled robots, smartphones, and mixed reality headsets based on visual SLAM. - Computer Vision, Intelligent Robotics
- Bachelor Thesis, Master Thesis, Semester Project
| Fast moving objects are defined as objects that move over significant distances over exposure time of a single image or video frame. Thus, they look significantly blurred. Detection, tracking, and deblurring of such objects have been studied in recent years. However, there are still no methods for robust retrieval of such objects in large image collections. - Computer Graphics, Computer Vision, Image Processing, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition
- Master Thesis
| Extend the recent Marigold in different aspects - Computer Vision
- Master Thesis
| The goal of this project is to implement an 6DoF object pose estimation method that utilizes the embedded sensors of head-mounted devices like the Microsoft HoloLens to improve the accuracy of the 6DoF pose estimation. The proposed method will be thoroughly evaluated and compared against single-view, stereo, and multi-view baselines. - Computer Vision
- ETH Zurich (ETHZ), Master Thesis
| Tetra-NeRF [1] offers a way to represent the scene as Delaunay tetrahedralization of the input point cloud. This can be used to represent dynamic 3D scenes [2] as the deformation is performed on the vertices of the tetrahedral mesh. - Computer Vision
- Bachelor Thesis, Master Thesis
| Motivation: Explore the newly improved Habitat 3.0 simulator with a special focus on the Virtual Reality Features.
This project is meant to be an exploration task on the Habitat 3.0 simulator, exploring all the newly introduced features focusing specifically on the implementation of virtual reality tools for scene navigation. The idea is to extend these features to self created environments in Unreal Engine that build uppon Habitat - Artificial Intelligence and Signal and Image Processing
- Semester Project
| Motivation: Create a realistic rendered benchmark to evaluate reinforcement agents, visual navigation tasks, interaction with other agents, navigation in scene with static and dynamic objects and humans.
How: Create a realistic rendered benchmark to evaluate reinforcement agents, visual navigation tasks, interaction with other agents, navigation in scene with static and dynamic objects and humans. - Artificial Intelligence and Signal and Image Processing
- Master Thesis
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