 Computer Vision and Geometry GroupOpen OpportunitiesThis project reconstructs liquids from multi-view imagery, segmenting fluid regions using methods like Mask2Former and reconstructing static scenes with 3D Gaussian Splatting or Mast3r. The identified fluid clusters initialize a particle-based simulation, refined for temporal consistency and enhanced by optional thermal data and visual language models for fluid properties. - Computer Vision
- Master Thesis, Semester Project
| This project extends previous work [a] on calculating similarity scores between text prompts and 3D scene graphs representing environments. The current method identifies potential locations based on user descriptions, aiding human-agent communication, but is limited by its coarse localization and inability to refine estimates incrementally. This project aims to enhance the method by enabling it to return potential locations within a 3D map and incorporate additional user information to improve localization accuracy incrementally until a confident estimate is achieved.
[a] Chen, J., Barath, D., Armeni, I., Pollefeys, M., & Blum, H. (2024). "Where am I?" Scene Retrieval with Language. ECCV 2024. - Computer Vision
- Master Thesis, Semester Project
| The goal of this project is to enhance the 3D mapping capabilities of a robotic agent by incorporating uncertainty measures into MAP-ADAPT, an incremental mapping pipeline that constructs an adaptive voxel grid from RGB-D input. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| The goal of the project is to create a Simultaneous Localization and Mapping algorithm that, besides estimating the camera trajectory and the geometry of the scene, also obtains object instances. These object instances should not be restricted to a fixed set of classes (e.g., chair, table). Hence, the problem is open set segmentation. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| MOTIVATION ⇾ Creating a digital twin of the robot's environment is crucial for several reasons:
1. Simulate Different Robots: Test various robots in a virtual environment, saving time and resources.
2. Accurate Evaluation: Precisely assess robot interactions and performance.
3. Enhanced Flexibility: Easily modify scenarios to develop robust systems.
4. Cost Efficiency: Reduce costs by identifying issues in virtual simulations.
5. Scalability: Replicate multiple environments for comprehensive testing.
PROPOSAL
We propose to create a digital twin of our Semantic environment, designed in your preferred graphics Platform to be able to simulate Reinforcement Learning agents in the digital environment, to create a unified evaluation platform for robotic tasks. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| Motivation ⇾ There are three ways to evaluate robots for pick-and-place tasks at home:
1. Simulation setups: High reproducibility but hard to simulate real-world complexities and perception noise.
2. Competitions: Good for comparing overall systems but require significant effort and can't be done frequently.
3. Custom lab setups: Common but lead to overfitting and lack comparability between labs.
Proposal ⇾ We propose using IKEA furniture to create standardized, randomized setups that researchers can easily replicate. E.g, a 4x4 KALLAX unit with varying door knobs and drawer positions, generating tasks like "move the cup from the upper right shelf into the black drawer." This prevents overfitting and allows for consistent evaluation across different
labs. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| MOTIVATION
Most 3D scene understanding work applied in the field of robotics realy on two main assumptions:
1. Detailed and accurate 3D reconstructions
2. Reliable semantic segmentation
PROPOSAL
We propose to use the robot itself for mapping, and then performing the Semantic Segmentation task on the on-board computer. This will allow us to have an end-to-end pipeline to perform scen understanding in real time on the Spot robot. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| Motivation ⇾ We want to train robots to interact in everyday home environments. But the robot needs data to learn from.
1. The robot needs data from humans to naturally interact with the environment.
2. We need ground-truth of the interaction to evaluate methods.
3. The setup needs to be robust and versatile such that we can make many recordings.
Proposal ⇾ We want to develop a 3D ground-truth methodology for environment interactions.
We need a setup that is easier to transport than the classical “camera domes” that just record dynamic scenes from every angle. Instead we combine static scans with egocentric and a few exocentric video cameras.
Our goal is to be able to track the dynamic states of the functional elements within 1 cm accuracy. With this we can then go to any home and record interactions with high accuracy. - Artificial Intelligence and Signal and Image Processing
- Master Thesis, Semester Project
| This project aims to integrate loop closure optimization into voxel-based 3D mapping by developing a method to warp the voxel grid in response to updated camera trajectories. This approach eliminates the need to rebuild the map from scratch, enhancing the efficiency and adaptability of 3D mapping in real-world scenarios. - Computer Vision, Intelligent Robotics
- Master Thesis, Semester Project
| MOTIVATION
Neural Radiance Fields (NeRFs) require a substantial amount of data from various viewpoints to achieve high-quality reconstructions. This is because NeRFs rely on capturing the intricate details of a scene by learning the light field and volumetric density from multiple angles. Diverse data helps the model understand the scene's geometry, texture, and lighting, allowing it to render detailed and realistic views.
PROPOSAL
We propose to use MaRiNER [Bösiger et al. 2024] as a post-processing step to enhance NeRF reconstructions performed with a smaller amount of data. - Artificial Intelligence and Signal and Image Processing
- Semester Project
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