Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

Collisions in Multiple Dimensions: Problem Solving

Collisions in Multiple Dimensions: Problem Solving

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...

Modeling and Similitude

Modeling and Similitude

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...

Three-Dimensional Force System:Problem Solving

Three-Dimensional Force System:Problem Solving

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

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

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 10, 2025

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generation.

Bohan Li, Xin Jin, Jianan Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 25, 2025

Summary

This summary is machine-generated.

OccScene unifies 3D scene generation and perception using a novel mutual learning framework. This approach enhances both tasks by integrating semantic occupancy and text prompts for realistic scene creation and improved 3D perception.

More Related Videos

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

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

Published on: November 30, 2022

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

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

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Sep 10, 2025

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

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

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

Published on: November 30, 2022

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

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

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence
3D Graphics

Background:

Diffusion models excel in 3D scene generation and perception but are typically separated.
Existing methods often use synthetic data augmentation for perception tasks, limiting integration.

Purpose of the Study:

To propose OccScene, a unified framework for integrated 3D perception and generation.
To achieve synergistic improvements in both generation quality and perception accuracy through mutual learning.

Main Methods:

Developed OccScene, a joint-training diffusion framework guided by semantic occupancy and text prompts.
Introduced a Mamba-based Dual Alignment module to integrate fine-grained semantics and geometry as perception priors.
Enabled mutual learning where generation improves perception and vice-versa.

Main Results:

OccScene generates realistic and consistent 3D scenes from text prompts.
Demonstrated substantial performance improvements in semantic occupancy prediction.
Validated effectiveness across diverse indoor and outdoor scenarios.

Conclusions:

OccScene successfully integrates 3D scene generation and perception in a single framework.
The mutual learning paradigm offers significant benefits for both tasks.
Presents a new direction for developing advanced 3D understanding and creation systems.