3 citations · 4 across the 4 of their papers we have counts for
4 papers
FlowADMM: Plug-and-play ADMM with Flow-based Renoise-Denoise Priors
Hendrik Sommerhoff, Michael Moeller
Plug-and-play (PnP) methods for solving inverse problems have recently achieved strong performance by leveraging denoising priors based on powerful generative diffusion and flow mo…
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Saleh +3
The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion. Yet, several design cho…
Neural Volume Super-Resolution
Yuval Bahat, Yuxuan Zhang, Hendrik Sommerhoff +2
Neural volumetric representations have become a widely adopted model for radiance fields in 3D scenes. These representations are fully implicit or hybrid function approximators of…
Light Field Implicit Representation for Flexible Resolution Reconstruction
Paramanand Chandramouli, Hendrik Sommerhoff, Andreas Kolb
Inspired by the recent advances in implicitly representing signals with trained neural networks, we aim to learn a continuous representation for narrow-baseline 4D light fields. We…