Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

787
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
787
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

568
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
568
Deconvolution01:20

Deconvolution

647
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
647
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

681
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
681
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

1.4K
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
1.4K
Diffusion01:21

Diffusion

6.9K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.9K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Functional Characterization of HvGRK2 in Metamorphic Development and 20E-Related Signaling in Heortia vitessoides.

Archives of insect biochemistry and physiology·2026
Same author

Idiopathic mesenteric phlebosclerosis: A case report.

Medicine·2026
Same author

Fabrication of water-in-oil high internal phase emulsions containing astringent compounds for enhanced oral sensation.

Food & function·2026
Same author

Amino acid metabolism modulates macrophage polarization: implications for autoimmune-related diseases.

Frontiers in immunology·2026
Same author

Gut microbiota drives the metabolic dysregulation in obesity-prone individuals by impairing GDCA-mediated activation of brown adipose thermogenesis and ileal GLP-1 secretion.

Acta pharmaceutica Sinica. B·2026
Same author

How periodic decorations in extra-long highway tunnels affect driving safety: A simulation study.

Traffic injury prevention·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Ver todos los artículos relacionados
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Video Experimental Relacionado

Updated: Mar 1, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

815

ReconX: Reconstruye cualquier escena a partir de vistas dispersas con un modelo de difusión de vídeo

Fangfu Liu, Wenqiang Sun, Hanyang Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 27, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    ReconX aborda la reconstrucción de escenas 3D con vistas dispersas tratándola como una tarea de generación de vídeo. Este novedoso enfoque aprovecha los grandes modelos de difusión de vídeo para crear escenas 3D consistentes y detalladas a partir de imágenes limitadas.

    Palabras clave:
    Reconstrucción 3DVistas dispersasModelos de difusión de vídeoGeneración de vídeoConsistencia 3DGráficos por computadoraInteligencia artificial

    Más Videos Relacionados

    Computer-Generated Animal Model Stimuli
    26:43

    Computer-Generated Animal Model Stimuli

    Published on: July 29, 2007

    11.4K
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    16.1K

    Videos de Experimentos Relacionados

    Last Updated: Mar 1, 2026

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    815
    Computer-Generated Animal Model Stimuli
    26:43

    Computer-Generated Animal Model Stimuli

    Published on: July 29, 2007

    11.4K
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    16.1K

    Área de la Ciencia:

    • Visión por Computadora
    • Gráficos 3D
    • Inteligencia Artificial

    Sus antecedentes:

    • La reconstrucción de modelos 3D a partir de imágenes 2D muestra un gran éxito en escenarios de vistas densas.
    • La reconstrucción 3D de vistas dispersas sigue siendo un problema mal planteado, lo que genera artefactos y distorsiones.

    Objetivo del estudio:

    • Proponer ReconX, un nuevo paradigma para la reconstrucción de escenas 3D a partir de vistas dispersas.
    • Abordar el desafío de la consistencia de vistas 3D en modelos generativos para la reconstrucción.

    Principales métodos:

    • Replantear la reconstrucción de vistas dispersas como una tarea de generación temporal utilizando modelos de difusión de vídeo preentrenados.
    • Codificar una nube de puntos global como una condición de estructura 3D para guiar la síntesis de fotogramas de vídeo.
    • Emplear la Mapeo Gaussiano 3D (3D Gaussian Splatting) consciente de la confianza para la recuperación final de la escena 3D a partir del vídeo generado.

    Principales resultados:

    • ReconX sintetiza fotogramas de vídeo con preservación de detalles y alta consistencia 3D a partir de vistas de entrada limitadas.
    • El método supera eficazmente los artefactos y las distorsiones comunes en la reconstrucción de vistas dispersas.
    • Demuestra una calidad y generalización superiores en comparación con los métodos de vanguardia en conjuntos de datos del mundo real.

    Conclusiones:

    • ReconX ofrece un nuevo y potente enfoque para la reconstrucción de escenas 3D a partir de vistas dispersas.
    • El aprovechamiento de los *priors* generativos de los modelos de difusión de vídeo mejora la precisión y la consistencia de la reconstrucción.
    • El método propuesto avanza el campo de la reconstrucción 3D, particularmente para escenarios desafiantes de vistas dispersas.