github/jentuuh linkedin/jentevandersanden jente.vds.pro@gmail.com

About me

I am a second-year PhD candidate at MPI-INF (Saarbrücken), supervised by Gurprit Singh and Hans-Peter Seidel. At the end of my Master studies, I also had the privilege of interning at Wenzel Jakob’s Realistic Graphics Lab at EPFL (Switzerland). Before that, I completed my undergraduate studies with a major in Visual Computing at Hasselt University (Belgium).

My research interests include, but are not limited to generative rendering, physically-based (differentiable) rendering, inverse (graphics) problems and real-time global illumination. I am currently working to bridge the gap between the broad pixel-space knowledge of 2D image-based diffusion models and the world-space understanding inherent in 3D graphics renderers.

Publications

A Scalable and Coherent Approach to Monte Carlo Path Tracing for Multi-User Environments

Jente Vandersanden

Hasselt University (MSc Thesis, 2023)

Project page | Paper

An adaptation of the Monte-Carlo path tracing algorithm that makes it more modular and scalable for multi-user cloud-gaming environments.

Shared Rendering Computation in Cloud Gaming

Jente Vandersanden

Hasselt University (BSc Thesis, 2021)

Project page | Paper

A shared rendering system rendering diffuse interactive global illumination for dynamic scenes on a centralized server.

Other research projects

During my time at the Realistic Graphics Lab at EPFL in the summer of 2023, I worked together with the Mitsuba 3 team to implement physically-accurate differentiable rendering of heightfields. This technique can for example be used to reconstruct landscape geometry from satellite images. In addition, an adaptation of the projective Sampling method of [Zhang et al. 2023] was implemented for heightfields. A brief demonstration can be found here.

During the summer of 2022, I was an intern for Prof. dr. Philippe Bekaert's Visual Computing research group at Hasselt University. My main contribution there was the development of a synthetic data generation pipeline that renders photorealistic novel views of an object by querying a neural radiance field. The tool was created with the purpose to generate training data for an object detection model tailored to specular objects.

Extracurricular

When I am not tracing light paths, I like to travel and experience other cultures. The beauty of nature and our planet keeps surprising me (that is another reason why I enjoy simulating it on a computer). I enjoy a few sports, under which Muay Thai and snowboarding. I am also a great enthusiast of (visual) art, whether it be video games, photography or cinematography. One of my aspirations is to one day find and pursue a personal project that unites my technical knowledge with my passion for these arts. Below you can find a random selection of pictures from the past few years.