1 code implementation • CVPR 2023 • Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes Kopf, Matthew O'Toole, Changil Kim
Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques.
Ranked #1 on Novel View Synthesis on DONeRF: Evaluation Dataset
1 code implementation • 2 Dec 2021 • Benjamin Attal, Jia-Bin Huang, Michael Zollhoefer, Johannes Kopf, Changil Kim
Our method supports rendering with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel.
1 code implementation • ECCV 2020 • Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, James Tompkin
Our approach is to simultaneously learn depth and disocclusions via a multi-sphere image representation, which can be rendered with correct 6DoF disparity and motion parallax in VR.
1 code implementation • NeurIPS 2021 • Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan, Changil Kim, Christian Richardt, James Tompkin, Matthew O'Toole
Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e. g., NeRF).
no code implementations • CVPR 2022 • Benjamin Attal, Jia-Bin Huang, Michael Zollhöfer, Johannes Kopf, Changil Kim
Our method supports rendering with a single network evaluation per pixel for small baseline light fields and with only a few evaluations per pixel for light fields with larger baselines.
no code implementations • ICCV 2023 • Aarrushi Shandilya, Benjamin Attal, Christian Richardt, James Tompkin, Matthew O'Toole
We present an image formation model and optimization procedure that combines the advantages of neural radiance fields and structured light imaging.