Department of Computer ScienceAcronym | D-INFK | Homepage | http://www.inf.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | Department of Computer Science | Child organizations | |
Open OpportunitiesThe 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
| What optimizations are necessary to make reflective PPG sensors reliably work on tissue with limited blood perfusion?
Note: Candidates should have experience in hardware design (analog circuits, embedded systems, and basic signal processing). - Electrical and Electronic Engineering, Information, Computing and Communication Sciences
- 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
| In the burgeoning field of deep reinforcement learning (RL), agents autonomously develop complex behaviors through a process of trial and error. Yet, the application of RL across various domains faces notable hurdles, particularly in devising appropriate reward functions. Traditional approaches often resort to sparse rewards for simplicity, though these prove inadequate for training efficient agents. Consequently, real-world applications may necessitate elaborate setups, such as employing accelerometers for door interaction detection, thermal imaging for action recognition, or motion capture systems for precise object tracking. Despite these advanced solutions, crafting an ideal reward function remains challenging due to the propensity of RL algorithms to exploit the reward system in unforeseen ways. Agents might fulfill objectives in unexpected manners, highlighting the complexity of encoding desired behaviors, like adherence to social norms, into a reward function.
An alternative strategy, imitation learning, circumvents the intricacies of reward engineering by having the agent learn through the emulation of expert behavior. However, acquiring a sufficient number of high-quality demonstrations for this purpose is often impractically costly. Humans, in contrast, learn with remarkable autonomy, benefiting from intermittent guidance from educators who provide tailored feedback based on the learner's progress. This interactive learning model holds promise for artificial agents, offering a customized learning trajectory that mitigates reward exploitation without extensive reward function engineering. The challenge lies in ensuring the feedback process is both manageable for humans and rich enough to be effective. Despite its potential, the implementation of human-in-the-loop (HiL) RL remains limited in practice. Our research endeavors to significantly lessen the human labor involved in HiL learning, leveraging both unsupervised pre-training and preference-based learning to enhance agent development with minimal human intervention. - Engineering and Technology, Information, Computing and Communication Sciences
- 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
| Latent diffusion models (LDMs) [1] have recently emerged as a powerful tool for high-quality image generation, offering superior scalability and training efficiency compared to pixel-space diffusion models. While the network architectures of LDMs have received significant attention, other design aspects of these models (for example the forward noise schedule and the autoencoder) remain underexplored. This project aims to enhance the characteristics of LDMs, e.g., quality and efficiency, by investigating various design elements of latent diffusion models.
- Information, Computing and Communication Sciences
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| 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
| Digital capture of human bodies is a rapidly growing research area in computer vision and computer graphics that puts scenarios such as life-like mixed-reality (MR) virtual-social interactions into reach. Therefore, we offer projects for modeling and capturing humans at the intersection of computer vision, computer graphics, and machine learning. - Computer Graphics, Computer Vision, Virtual Reality and Related Simulation
- Master Thesis, Semester Project
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