Jove
Visualize
Contáctanos
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

Videos de Conceptos Relacionados

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

900
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
900
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Modeling and Similitude01:12

Modeling and Similitude

329
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
329
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

854
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
854

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Comparison of Allo-HSCT outcomes after CAR-T therapy versus chemotherapy in pediatric patients with relapsed/refractory B-ALL: a retrospective study.

The oncologist·2026
Same author

Research on Density Prediction of Laser Powder Bed Fusion Process Parameters for IN718 Nickel-Based Superalloy Based on Machine Learning.

Materials (Basel, Switzerland)·2026
Same author

LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Dynamics and Mechanism of Photoenzymatic Dehalogenation Reactions through Electron-Transfer Bifurcation.

Journal of the American Chemical Society·2026
Same author

Smartly engineered biomaterials drive immune remodeling: A new paradigm for precise treatment of inflammatory bowel disease.

International immunopharmacology·2026
Same author

Mitigating multimodal hallucinations through visual attention tracing and origin-point regeneration.

Scientific reports·2026
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Sep 10, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

160

OccScene: Aprendizaje mutuo basado en la ocupación semántica para la generación de escenas en 3D

Bohan Li, Xin Jin, Jianan Wang

    IEEE transactions on pattern analysis and machine intelligence
    |August 25, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    OccScene unifica la generación y la percepción de escenas 3D utilizando un nuevo marco de aprendizaje mutuo. Este enfoque mejora ambas tareas al integrar la ocupación semántica y los mensajes de texto para la creación de escenas realistas y una mejor percepción 3D.

    Más Videos Relacionados

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    635

    Videos de Experimentos Relacionados

    Last Updated: Sep 10, 2025

    Photorealistic Learned Landscapes for Augmented Reality
    06:54

    Photorealistic Learned Landscapes for Augmented Reality

    Published on: June 27, 2025

    160
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    635

    Área de la Ciencia:

    • Visión por computadora
    • Inteligencia artificial
    • Gráficos en 3D

    Sus antecedentes:

    • Los modelos de difusión se destacan en la generación y percepción de escenas en 3D, pero generalmente están separados.
    • Los métodos existentes a menudo utilizan el aumento de datos sintéticos para tareas de percepción, lo que limita la integración.

    Objetivo del estudio:

    • Proponer OccScene, un marco unificado para la percepción y generación 3D integradas.
    • Lograr mejoras sinérgicas tanto en la calidad de generación como en la precisión de la percepción a través del aprendizaje mutuo.

    Principales métodos:

    • Desarrolló OccScene, un marco de difusión de capacitación conjunta guiado por la ocupación semántica y las indicaciones de texto.
    • Se introdujo un módulo de alineación dual basado en Mamba para integrar la semántica y la geometría de grano fino como priores de percepción.
    • Habilitado el aprendizaje mutuo donde la generación mejora la percepción y viceversa.

    Principales resultados:

    • OccScene genera escenas 3D realistas y consistentes a partir de instrucciones de texto.
    • Demostró mejoras sustanciales en el rendimiento de la predicción de ocupación semántica.
    • Efectividad validada en diversos escenarios interiores y exteriores.

    Conclusiones:

    • OccScene integra con éxito la generación y la percepción de escenas 3D en un único marco.
    • El paradigma de aprendizaje mutuo ofrece beneficios significativos para ambas tareas.
    • Presenta una nueva dirección para el desarrollo de sistemas avanzados de comprensión y creación 3D.