Benjamin Attal

I'm entering my 6th year as a PhD student at Carnegie Mellon University, where I'm advised by Professor Matthew O'Toole. Previously, I received my Bachelor's and Master's degrees in Computer Science and Applied Math from Brown University. I'm supported by the Meta PhD fellowship in AR/VR Computer Graphics.

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Research

My research lies at the intersection of computer vision, computational imaging, and machine learning. I am interested in leveraging physics-based light transport and neural fields to design robust systems for inverse rendering and 3D reconstruction.

A more physically-accurate inverse rendering system based on radiance caching for recovering geometry, materials, and lighting from RGB images of an object or scene.

C-ToF depth cameras can't reconstruct dynamic objects well. We fix that with our NeRF model that takes raw ToF signal and reconstructs motion along with the depth.

We apply a neural volume rendering framework to the raw images from structured-light sensors in order to achieve high-quality 3D reconstruction.

A 6-DoF video pipeline based on neural radiance fields that achieves a good trade-off between speed, quality, and memory efficiency. It excels at representing challenging view-dependent effects such as reflections and refractions.

A fast and compact neural field representation for light fields.

A practical coded diffraction imaging framework that can decouple mutually incoherent mixed-states, such as different wavelengths. Applications in computational microscopy.

We apply a phasor volume rendering model to the raw images from C-ToF sensors in order to achieve high-quality 3D torfstruction of static and dynamic scenes.

We build a real-time inference and rendering framework for 6-DoF video based on multi-sphere images.