| title | categories | tags | website | github | publication_id | image | permalink | redirect_from | |||||||||||||
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ICMLCN2025 - Towards Generative Ray Path Sampling for Faster Point-to-Point Ray Tracing |
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jeertmans/DiffeRT |
icmlcn2025 |
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/posts/icmlcn2025/ |
/posts/icmlcn2025-presentation/ |
Posters and 3-minute thesis presented at ICMLCN 2025!
At ICMLCN 2025, I presented our paper Towards Generative Ray Path Sampling for Faster Point-to-Point Ray Tracing {% cite Eert2505:Comparing %}. This work is the result of a collaboration with Professors Vittorio Degli-Esposti and Enrico Maria Vitucci's laboratory from University of Bologna, where I worked for 4 months in late 2024. In this work, we introduce a novel Machine Learning approach to Ray Tracing: sampling path candidates using a generative model. The core motivation is to avoid the exponential complexity of generating all path candidates by using a surrogate model that learns how to only suggest the most promising candidate rays, i.e., the candidates are likely to generate a physically valid ray path.
My participation to ICMLCN was also a great opportunity to demonstrate DiffeRT, the open-source Ray Tracing tool I develop for my research, so I prepared a small poster for that too. I also participated in the on-site 3-minute thesis contest.
Finally, an interactive tutorial is available to guide the readers through the implementation and training of our presented model.
Below, you can find the different media I used to present my research at ICMLCN.
<iframe src="/assets/pdf/2025-05-27-icmlcn-paper-poster.pdf" width="100%" height="415px" allowfullscreen></iframe> <iframe src="/assets/pdf/2025-05-27-icmlcn-demo-poster.pdf" width="100%" height="415px" allowfullscreen></iframe>I also made a small 1-minute video showcasing DiffeRT's main features.
{% include embed/youtube.html id='KANntIi1hjs' %}
Unfortunately, the on-site contest was not recorded, but you can find the video I recorded when submitting my participation to the contest.
{% include embed/youtube.html id='o76xb-Dw5yE' %}
{% bibliography --cited %}