EE Seminar: HydroNeRF: NeRF for Scenes with Air and Water
Electrical Engineering Systems Seminar
Speaker: Osher Stamker
M.Sc. student under the supervision of Prof. Shai Avidan & Prof. Alex Liberzon
Wednesday, 13th November 2024, at 15:30
Room 011, Kitot Building, Faculty of Engineering
HydroNeRF: NeRF for Scenes with Air and Water
Abstract
Neural Radiance Fields (NeRF) revolutionized the field of view synthesis by providing high-quality, photorealistic novel views from unstructured image collections. As a side benefit, it is possible to measure geometric properties in the scene (such as distances between 3D points) based on a depth map that is extracted from NeRF.
However, NeRF models struggle with complex light interactions, such as reflections and refractions, often resulting in inaccurate renderings. Going beyond view synthesis, accurate 3D measurements of objects submerged in liquid is also important for fluid mechanics.
In this work, we introduce HydroNeRF, an enhanced NeRF framework designed to accurately model light refraction and handle reflective scenes. Our approach leverages Snell's law to model the bending of light rays as they transition between different media (air, glass, water), and integrates a Deformation Network to render the scene.
We collected a dataset of challenging scenes that contain objects submerged in water tanks and show that HydroNeRF can be used to measure distances between 3D points more accurately than NeRF.
השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בדף הנוכחות שיועבר באולם במהלך הסמינר
