3 papers
cs.LG2025
MAP Estimation with Denoisers: Convergence Rates and Guarantees
Scott Pesme, Giacomo Meanti, Michael Arbel +1
Denoiser models have become powerful tools for inverse problems, enabling the use of pretrained networks to approximate the score of a smoothed prior distribution. These models are…
cs.CV2025
Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
Giacomo Meanti, Thomas Ryckeboer, Michael Arbel +1
This work addresses image restoration tasks through the lens of inverse problems using unpaired datasets. In contrast to traditional approaches -- which typically assume full knowl…
cs.CV2024
LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes
Juliette Marrie, Romain Menegaux, Michael Arbel +2
We address the problem of extending the capabilities of vision foundation models such as DINO, SAM, and CLIP, to 3D tasks. Specifically, we introduce a novel method to uplift 2D im…