Geometrically-Conditioned Point Diffusion Models

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Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

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 community. Here, we tackle the related problem of generating point clouds, both uncondition ...

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 recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

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 ...

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

In this manuscript, we present a collective multigrid algorithm to solve efficiently the large saddle-point systems of equations that typically arise in PDE-constrained optimization under uncertainty, and develop a novel convergence analysis of collective ...

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group ...

We present a pipeline for the automatic generation of digital twins of free-standing buildings for damage assessment. Our method takes multiple view images of the target building as input and processes them to produce a 3D model in levels of detail (LOD) f ...

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captu ...

This paper presents explicit solutions for two related non-convex information extremization problems due to Gray and Wyner in the Gaussian case. The first problem is the Gray-Wyner network subject to a sum-rate constraint on the two private links. Here, ou ...

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 ...

In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the worst distribution from within a known ambiguity set. A common shortcoming o ...

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

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 ...

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 ...

Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

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 ...

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn th ...

We present a method for hiding images in synthetic videos and reveal them by temporal averaging. We developed a visual masking method that hides the input image both spatially and temporally. Our masking approach consists of temporal and spatial pixel by p ...

Geometrically-Conditioned Point Diffusion Models

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Aggregating Spatial and Photometric Context for Photometric Stereo

GECCO: Geometrically-Conditioned Point Diffusion Models

The role of secondary ice production in mixed-phase clouds

Towards learning-based denoising of light fields

Center-Based Decoupled Point Cloud Registration for 6D Object Pose Estimation

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

A multigrid method for PDE-constrained optimization with uncertain inputs

A Wasserstein-based measure of conditional dependence

Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

On combining denoising with learning-based image decoding

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

Learning stereo reconstruction with deep neural networks

LMDZ6A: The Atmospheric Component of the IPSL Climate Model With Improved and Better Tuned Physics

An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol-cloud-radiation interactions in the southeast Atlantic basin

Deep Image Restoration: Between Data Fidelity and Learned Priors

Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Synthetic electronic video containing a hidden image