Bachelor, Master and Doctoral School Projects
- Structured 3D Shape Optimization with Part-Based Implicit Neural Representations
- Recovering Thin Structures in 3D Foundation Models
- Applied Deep Learning for Computer Aided Engineering
- Real-Time 3D Tracking and Anomaly Detection for Maritime Kite Systems
- Efficient Diffusion Models via Quantization and Compression
- 3D Surface reconstruction using 3D Gaussians and SDF
- Computer Vision for Waste Management (CVLab x Wasteflow)
For further project offers, please contact members of the CVLAB directly.
Job Offers
None available at the moment.
Administrative
Semester Projects (Bachelor and Master)
SIN and SSC students do one semester project during their Bachelor studies and one semester project during their Master studies.
Semester projects can be done in groups of two students.
Semester projects are worth 8 credits for Bachelor and 12 credits for Master.
Students must have the approval of the Professor in charge of the laboratory before registering for the given project.
Oral defense: within two weeks of the hand-in date.
Master Thesis Projects
Master Thesis Projects are started once the complete master program is finished and all the credits have been obtained.
Projects for SSC and SIN students should last 4 months at the EPFL or 6 months in the industry or in another University.
Master Thesis Projects must be done individually.
Master Thesis Projects are worth 30 credits.
Students must have the approval of the Professor in charge of the laboratory before registering for the given project.
Additional information
Structured 3D Shape Optimization with Part-Based Implicit Neural Representations
Part-based implicit representation. (Left) An auto-decoder network fθ decodes latent vectors and poses into the parts of a shape. (Right) It can be used to generate new shapes given a coarse part configuration (a), to edit individual parts (b), and do part-based optimization (c). (Taken from [1].)Modern engineering design requires structured, modular, and optimizable 3D (…)
Recovering Thin Structures in 3D Foundation Models
Recently, the task of 3D reconstruction has been revolutionized by the advent of 3D foundation models that predict scene geometry and camera parameters using only a transformer-based feed-forward network. However, transformers are known to be less effective on thin structures, primarily because of the rough patchification. In this project, we will investigate how to improve (…)
Real-Time 3D Tracking and Anomaly Detection for Maritime Kite Systems
At Aether Swiss Kite, we are developing an automated, plug-and-play kite wing system designed to decarbonize the maritime industry. To ensure the system’s safety and reliability, we are implementing a 360° vision- based redundancy system for real-time kite tracking. Building upon a previous semester project that achieved reliable 2D detection, trajectory mapping, and orientation tracking, (…)
3D Surface reconstruction using 3D Gaussians and SDF
3D Gaussian Splatting, while being primarily a novel view synthesis method, is adapted for 3D surface reconstruction by methods such as SuGaR. Some methods try to reconstruct the surface as an ad-hoc method while some train a traditional neural SDF in conjunction with the 3D Gaussians. In this project, the goal is to try to (…)
Applied Deep Learning for Computer Aided Engineering
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.
Efficient Diffusion Models via Quantization and Compression
Diffusion models, such as Stable Diffusion, have achieved state-of-the-art results in generative image synthesis. However, they require hundreds of millions (or even billions) of parameters, making them computationally expensive and memory-heavy. This hinders deployment on resource-constrained devices like mobile phones or edge accelerators. Objective: Explore and systematically evaluate quantization and compression methods tailored for diffusion (…)