Bachelor, Master and Doctoral School Projects

Recent advances in neuroscience have enabled researchers to visualize the activity patterns of up to thousands of neurons simultaneously. These experiments produce large 4-dimensional (time plus XYZ) or 5-dimensional (time plus XYZ plus multiple colors) image datasets capturing neurons changing in brightness as they become more or less active. Processing these datasets efficiently and accurately (…)

Over the decades and centuries, many statues displayed in cultural monuments have been damaged. The goal of this project therefore is to study the use of the recent developments in Diffusion Model, Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) to digitally reconstruct in 3D the head of the Virgin with the Child statue (…)

Counterfactual explanations  (CEs) aim to alter output labels with minimal disruption. While adversarial attacks do it regardless of the nature of these changes, CEs restrain its attack to remain in the data manifold. To this end, current methods utilize diffusion processes ([1],[2]) for direct optimization, which, although effective, require significant memory and computational resources for (…)

Demonstrate spacecraft pose estimation algorithms running on small edge devices.

Modern 3D reconstruction techniques rely on pixel consistency across images to recover 3D geometry from 2D images. These methods include NeRFs and Gaussian Splatting, which are the most accurate techniques in current state-of-the-art. However, they struggle on inputs with little contrast, objects which are transparent or reflective, and views which are too few or with (…)

Explainable AI (XAI) seeks to tackle the opacity of deep neural network decisions. Moving beyond the conventional focus on 2D imagery ([1],[2]), our research provides the first method to provide Counterfactual Explanations (CEs) for 3D point cloud classifiers. Specifically, we propose for the first time a method for creating counterfactuals with explicit semantic guidance.

We live in a world full of manufactured objects of ever-increasing complexity that require clever engineering to be functional. Today, individual parts of these objects are often optimized separately, control is rarely accounted for at design time, and much manual tinkering is required. These shortcomings can largely be ascribed to the composite nature of industrially-designed (…)

In particular we are targeting the task of 3D tracking and long-term tracking of people. The first step toward that goal will be to build a multi-camera dataset suitable for those tasks. Initially the student will design a multi-view annotation tools that will leverage camera calibration to minimize annotation cost.

This project aims to enhance object detection and tracking algorithms by integrating scene geometry into the detection process. Utilizing mesh data of the scene and video footage from one or multiple cameras, the project seeks to develop a detection system that can leverage this additional information to improve accuracy and efficiency.

Computer Aided Engineering (CAE) is at the core of modern industrial engineering and manufacturing. However, the current CAE applications suffer from significant time and human resource expenses. Our goal is to leverage deep learning techniques to automate the CAE process and reduce the R&D costs for the industry.

The project presented here aims to address these limitations by fine-tuning a Large Language Model (LLM) to the specific cultural and linguistic characteristics of the Canton de Fribourg.

Develop a comprehensive framework for generating near-photorealistic images that accurately represent deformable objects and changing materials.