Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision com- munity. Here, we tackle the related problem of generating point clouds, both unconditi ...
Clouds are omnipresent in the Earth's atmosphere. Their phase composition significantly modulates their interaction with solar and terrestrial radiation, as well as precipitation formation. Particularly for clouds containing both phases, known as mixed-pha ...
In this paper, we propose a novel center-based decoupled point cloud registration framework for robust 6D object pose estimation in real-world scenarios. Our method decouples the translation from the entire transformation by predicting the object center an ...
Atmospheric models often fail to correctly reproduce the microphysical structure of Arctic mixed-phase clouds and underpredict ice water content even when the simulations are constrained by observed levels of ice nucleating particles. In this study we inve ...
Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their ...
The cloud parameterizations of the LMDZ6A climate model (the atmospheric component of the IPSL-CM6 Earth system model) are entirely described, and the global cloud distribution and cloud radiative effects are evaluated against the CALIPSO-CloudSat and CERE ...
Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling'' in the atmospheric sciences: improving the spatial resolution of low-resolution images. The abil ...
This study presents the version of the LMDZ global atmospheric model used as the atmospheric component of the Institut Pierre Simon Laplace coupled model (IPSL-CM6A-LR) to contribute to the 6th phase of the international Coupled Model Intercomparison Proje ...
We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks. We apply the CGAN to generating two-dimensional ...
The role of surfactants in governing water interactions of atmospheric aerosols has been a recurring topic in cloud microphysics for more than two decades. Studies of detailed surface thermodynamics are limited by the availability of aerosol samples for ex ...
A large part of computer vision research is devoted to building models
and algorithms aimed at understanding human appearance and behaviour
from images and videos. Ultimately, we want to build automated systems
that are at least as capable as people when i ...
The first results of a campaign of intensive observation of precipitation in Dumont d'Urville, Antarctica, are presented. Several instruments collected data from November 2015 to February 2016 or longer, including a polarimetric radar (MXPol), a Micro Rain ...
We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...
