Gradient Response Maps for Real-Time Detection of Textureless Objects

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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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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 ...

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Recent advances in image compression have made it both possible and desirable for image quality to approach the visually lossless range. However, the most commonly used subjective visual quality assessment protocols, e.g. those reported in ITU-T Rec. BT.50 ...

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The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...

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 ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

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Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis p ...

Gradient Response Maps for Real-Time Detection of Textureless Objects

Chattez avec Graph Search

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

3D diffractive optics for linear interconnects and nonlinear processing

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

Enabling Uncertainty Estimation in Iterative Neural Networks

Incorporating Projective Geometry into Deep Learning

Multimaterial Volumetric Printing of Silica-Based Glasses

Deep Learning for 3D Surface Modelling and Reconstruction

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

Detecting Anomalies and Obstacles in Road Scenes

On the assessment of high-quality images: advances on the JPEG AIC-3 activity

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

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Learning Transformations To Reduce the Geometric Shift in Object Detection

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

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata