Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Valsesia, Diego"'
There is growing interest towards the use of AI directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This paper presents a blueprint to the mission designer for the development of a modular a
Externí odkaz:
http://arxiv.org/abs/2408.09754
Generating videos with realistic and physically plausible motion is one of the main recent challenges in computer vision. While diffusion models are achieving compelling results in image generation, video diffusion models are limited by heavy trainin
Externí odkaz:
http://arxiv.org/abs/2405.13557
We present Stochastic Gaussian Splatting (SGS): the first framework for uncertainty estimation using Gaussian Splatting (GS). GS recently advanced the novel-view synthesis field by achieving impressive reconstruction quality at a fraction of the comp
Externí odkaz:
http://arxiv.org/abs/2403.18476
Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard of spacecrafts, due to the large computational complexity needed to achieve adequate representational power, as well as the lack of suitab
Externí odkaz:
http://arxiv.org/abs/2403.17677
Autor:
Aira, Luca Savant, Valsesia, Diego, Molini, Andrea Bordone, Fracastoro, Giulia, Magli, Enrico, Mirabile, Andrea
Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images carrying complementary information in the form of sub-pixel offsets in the scene sampling, and can be sign
Externí odkaz:
http://arxiv.org/abs/2401.16972
Denoising Diffusion Models (DDMs) have become a popular tool for generating high-quality samples from complex data distributions. These models are able to capture sophisticated patterns and structures in the data, and can generate samples that are hi
Externí odkaz:
http://arxiv.org/abs/2301.07969
Point clouds of 3D objects exhibit an inherent compositional nature where simple parts can be assembled into progressively more complex shapes to form whole objects. Explicitly capturing such part-whole hierarchy is a long-sought objective in order t
Externí odkaz:
http://arxiv.org/abs/2209.10318
In this paper we explore the recent topic of point cloud completion, guided by an auxiliary image. We show how it is possible to effectively combine the information from the two modalities in a localized latent space, thus avoiding the need for compl
Externí odkaz:
http://arxiv.org/abs/2209.09552
Inverse problems consist in reconstructing signals from incomplete sets of measurements and their performance is highly dependent on the quality of the prior knowledge encoded via regularization. While traditional approaches focus on obtaining a uniq
Externí odkaz:
http://arxiv.org/abs/2207.00460
Autor:
Valsesia, Diego, Magli, Enrico
Multi-image super-resolution from multi-temporal satellite acquisitions of a scene has recently enjoyed great success thanks to new deep learning models. In this paper, we go beyond classic image reconstruction at a higher resolution by studying a su
Externí odkaz:
http://arxiv.org/abs/2204.02631