On unifying sparsity and geometry for image-based 3D scene representation

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In this paper, we set the mathematical foundations of the Dynamical Low-Rank Approximation (DLRA) method for stochastic differential equations (SDEs). DLRA aims at approximating the solution as a linear combination of a small number of basis vectors with r ...

Fissures can have a significant impact on caprock integrity in the context of carbon capture and storage (CCS). In this campaign, the sealing capacity of a pre-fissured caprock material, the Opalinus Clay shale, is studied with real time x-ray imaging duri ...

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 ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual arti ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Silicate glasses have played a major role as structural and functional materials in human civilization since ancient Egypt. Despite their widespread use and importance in modern society, silica glasses with complex geometries are only fabricated in automat ...

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

We study the glass transition by exploring a broad class of kinetic rules that can significantly modify the normal dynamics of supercooled liquids while maintaining thermal equilibrium. Beyond the usual dynamics of liquids, this class includes dynamics in ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

We propose a semantic shape editing method to edit 3D triangle meshes using parametric implicit surface templates, benefiting from the many advantages offered by analytical implicit representations, such as infinite resolution and boolean or blending opera ...

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Tightly-coupled sensor orientation, i.e. the simultaneous processing of temporal (GNSS and raw inertial) and spatial (image and lidar) constraints in a common adjustment, has demonstrated significant improvement in the quality of attitude determination wit ...

Neurodegenerative and neuroinflammatory disorders often involve complex pathophysiological mechanisms that are â to this date â only partially understood. A more comprehensive understanding of those microstructural processes and their characterization ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

On unifying sparsity and geometry for image-based 3D scene representation

Chattez avec Graph Search

Dynamical Low-Rank Approximation for Stochastic Differential Equations

Sealing Response of Pre-Fissured Opalinus Clay to CO₂ Injection

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Incorporating Projective Geometry into Deep Learning

Subjective performance evaluation of bitrate allocation strategies for MPEG and JPEG Pleno point cloud compression

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Towards Trustworthy Deep Learning for Image Reconstruction

Multimaterial Volumetric Printing of Silica-Based Glasses

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Digital Twins of Stone Masonry Buildings for Damage Assessment

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Enabling Uncertainty Estimation in Iterative Neural Networks

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Semantic Shape Editing with Parametric Implicit Templates

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Robust machine learning for neuroscientific inference