Search Results for author: Matteo Maggioni

Found 13 papers, 7 papers with code

Global Latent Neural Rendering

no code implementations13 Dec 2023 Thomas Tanay, Matteo Maggioni

A recent trend among generalizable novel view synthesis methods is to learn a rendering operator acting over single camera rays.

Generalizable Novel View Synthesis Neural Rendering +1

Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations

1 code implementation CVPR 2023 Thomas Tanay, Aleš Leonardis, Matteo Maggioni

While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the way for a new paradigm relying on volumetric scene representations.

Denoising Novel View Synthesis

Residual Contrastive Learning: Unsupervised Representation Learning from Residuals

no code implementations29 Sep 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.

Contrastive Learning Image Reconstruction +3

Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images

no code implementations18 Jun 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.

Contrastive Learning Demosaicking +6

Pixel Adaptive Filtering Units

no code implementations24 Nov 2019 Filippos Kokkinos, Ioannis Marras, Matteo Maggioni, Gregory Slabaugh, Stefanos Zafeiriou

Next, we employ PAFU in deep neural networks as a replacement of standard convolutional layers to enhance the original architectures with spatially varying computations to achieve considerable performance improvements.


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