On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery

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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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To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models-a su ...

Systematic compositionality, or the ability to adapt to novel situations by creating a mental model of the world using reusable pieces of knowledge, remains a significant challenge in machine learning. While there has been considerable progress in the lang ...

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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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LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques. ...

An innovative photogrammetric pipeline has been developed by INAF-Padova for the processing of the stereo images from the CaSSIS (Colour and Stereo Imaging System) (Thomas et al., 2014). CaSSIS is the multispectral stereo push frame camera on board ExoMars ...

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...

On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery

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Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Enabling Uncertainty Estimation in Iterative Neural Networks

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

Automated Human Motion Analysis and Synthesis

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

Burst Vision Using Single-Photon Cameras

Detecting Anomalies and Obstacles in Road Scenes

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

Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models

Real-Time Localization for Closed-Loop Control of Assistive Furniture

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

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

Encoder-Decoder Models for Human Segmentation and Motion Analysis

Learning to Represent and Reconstruct 3D Deformable Objects

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Reshaping Perception for Autonomous Driving with Semantic Keypoints

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

3DPD: A photogrammetric pipeline for a PUSH frame stereo cameras

Perceiving Humans: from Monocular 3D Localization to Social Distancing