Benjamin Attal (original) (raw)

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. Email / CV / Scholar

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.

Your browser does not support the video tag.	Neural Inverse Rendering from Propagating Light Anagh Malik*, Benjamin Attal*, Andrew Xie, Matthew O'Toole, David B. Lindell CVPR, 2025 (Oral Presentation, Best Student Paper 🏆) project page /arXiv Time-resolved relighting and geometry estimation through radiance caching.
Your browser does not support the video tag.	Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering Benjamin Attal,Dor Verbin,Ben Mildenhall,Peter Hedman,Jonathan T. Barron,Matthew O'Toole,Pratul P. Srinivasan ECCV, 2024 (Oral Presentation) project page /paper 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.
Your browser does not support the video tag.	Flowed Time of Flight Radiance Fields Mikhail Okunev*,Marc Mapeke*,Benjamin Attal,Christian Richardt,Matthew O'Toole,James Tompkin ECCV, 2024 project page /paper 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.
Your browser does not support the video tag.	Neural Fields for Structured Lighting Aarrushi Shandilya ,Benjamin Attal,Christian Richardt,James Tompkin,Matthew O'Toole ICCV, 2023 project page /paper We apply a neural volume rendering framework to the raw images from structured-light sensors in order to achieve high-quality 3D reconstruction.
Your browser does not support the video tag.	HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling Benjamin Attal,Jia-Bin Huang,Christian Richardt,Michael Zollhoefer,Johannes Kopf,Matthew O'Toole,Changil Kim CVPR, 2023 (Highlight) project page /video /paper 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.
Your browser does not support the video tag.	Learning Neural Light Fields with Ray-Space Embedding Networks Benjamin Attal,Jia-Bin Huang,Michael Zollhoefer,Johannes Kopf,Matthew O'Toole,Changil Kim CVPR, 2022 project page /video /paper A fast and compact neural field representation for light fields.
Your browser does not support the video tag.	Towards Mixed-State Coded Diffraction Imaging Benjamin Attal,Matthew O'Toole TPAMI, 2022 project page /paper A practical coded diffraction imaging framework that can decouple mutually incoherent mixed-states, such as different wavelengths. Applications in computational microscopy.
Your browser does not support the video tag.	TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis Benjamin Attal,Eliot Laidlaw,Aaron Gokaslan,Christian Richardt,James Tompkin,Matthew O'Toole NeurIPS, 2021 project page /paper 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.
Your browser does not support the video tag.	MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images Benjamin Attal,Selena Ling,Aaron Gokaslan,Christian Richardt,James Tompkin, ECCV, 2020 (Oral Presentation) project page /video /paper We build a real-time inference and rendering framework for 6-DoF video based on multi-sphere images.