Learning to Reconstruct Texture-less Deformable Surfaces from a Single View

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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 ...

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In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...

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The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

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Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...

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Representing and reconstructing 3D deformable shapes are two tightly linked problems that have long been studied within the computer vision field. Deformable shapes are truly ubiquitous in the real world, whether be it specific object classes such as human ...

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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 ...

Learning to Reconstruct Texture-less Deformable Surfaces from a Single View

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Aggregating Spatial and Photometric Context for Photometric Stereo

3D diffractive optics for linear interconnects and nonlinear processing

Deep Learning for 3D Surface Modelling and Reconstruction

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

ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Exploring Novel Modalities for Optical Diffraction Tomography

Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Automated image-based generation of finite element models for masonry buildings

Importance of 3D radiative transfer effects on high-resolution NO2 remote sensing in cities

Learning to Represent and Reconstruct 3D Deformable Objects

2.5 Hz sample rate time-domain near-infrared optical tomography based on SPAD-camera image tissue hemodynamics

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Image-based geometric digital twinning for stone masonry elements

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Robust Isometric Non-Rigid Structure-From-Motion

Fast 3D Modeling of Prosthetic Robotic Hands Based on a Multi-Layer Deformable Design

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Bragg edge tomography characterization of additively manufactured 316L steel

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