Institute for Visual ComputingOpen OpportunitiesWe will explore the design space of avatars in Virtual Reality to support learning and creativity. The project will leverage the concept of "embodied cognition", a set of theories that imply that our bodies and their interaction with the environment can impact how we learn. We will develop a Unity3D-based VR environment for embodied learning that can be deployed on everyday VR headsets. - Computer Graphics, Computer-Human Interaction
- Master Thesis, Semester Project
| Object search is the problem of letting a robot find an object of interest. For this, the robot has to explore the environment it is placed into until the object is found. To explore an environment, current robotic methods use geometrical sensing, i.e. stereo cameras, LiDAR sensors or similar, such that they can create a 3D reconstruction of the environment which also has a clear distinction of 'known & occupied', 'known & unoccupied' and 'unknown' regions of space.
The problem of the classic geometric sensing approach is that it has no knowledge of e.g. doors, drawers, or other functional and dynamic elements. These however are easy to detect from images. We therefore want to extend prior object search methods such as https://naoki.io/portfolio/vlfm with an algorithm that can also search through drawers and cabinets. The project will require you to train your own detector network to detect possible locations of an object, and then implement a robot planning algorithm that explores all the detected locations. - Intelligent Robotics, Robotics and Mechatronics
- Master Thesis
| The objective of the project is to train a neural network taking any floorplan modality as input and outputting an embedding in a latent space shared by all the floorplan modalities. This is beneficial for downstream applications such as visual localization and model alignment. Check the attached the documents for more details.
The thesis will be co-supervised between CVG, ETH Zurich and Microsoft Spatial AI lab, Zurich. - Computer Vision
- ETH Zurich (ETHZ), Master Thesis
| This 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
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