Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

876
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...
876
Accelerating Fluids01:17

Accelerating Fluids

2.2K
When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
2.2K
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

972
Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
972
Velocity and Acceleration in Steady and Unsteady Flow01:11

Velocity and Acceleration in Steady and Unsteady Flow

523
In fluid mechanics, velocity and acceleration are key concepts for analyzing particle motion in both steady and unsteady flow. Consider a fluid particle moving along a pathline, where its velocity depends on its position and time. The particle's acceleration is obtained by differentiating the velocity with respect to time.
The acceleration can be generalized to any point in the flow, and expressed as components along three perpendicular directions, representing changes in velocity over...
523
Streamlines, Streaklines, and Pathlines01:18

Streamlines, Streaklines, and Pathlines

1.9K
A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over...
1.9K
Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

1.8K
Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Soil organic matter molecular composition with long-term detrital alterations is controlled by site-specific forest properties.

Global change biology·2022
Same author

Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages.

Sensors (Basel, Switzerland)·2021
Same author

Hollywood 3D: What are the Best 3D Features for Action Recognition?

International journal of computer vision·2020
Same author

Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos.

IEEE transactions on pattern analysis and machine intelligence·2019
Same author

HARD-PnP: PnP Optimization Using a Hybrid Approximate Representation.

IEEE transactions on pattern analysis and machine intelligence·2018
Same author

TMAGIC: A Model-Free 3D Tracker.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2017
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

Related Experiment Video

Updated: May 3, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.1K

Scene particles: unregularized particle-based scene flow estimation.

Simon Hadfield1, Richard Bowden1

  • 1University of Surrey, Guildford.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a fast algorithm for 3D scene flow estimation, outperforming existing methods by using multiple hypotheses instead of smoothness constraints for improved accuracy and speed.

More Related Videos

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.7K
Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

11.5K

Related Experiment Videos

Last Updated: May 3, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.1K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.7K
Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

11.5K

Area of Science:

  • Computer Vision
  • Robotics
  • 3D Scene Analysis

Background:

  • Optical flow estimates 2D motion, but 3D scene flow provides richer motion information.
  • Traditional methods often rely on smoothness constraints, leading to oversmoothing errors and slower computation.
  • Existing algorithms struggle with motion ambiguities and require isolated frame-by-frame estimation.

Purpose of the Study:

  • To develop a novel algorithm for estimating 3D scene flow that is significantly faster and more accurate than current state-of-the-art methods.
  • To address limitations of traditional smoothness constraints by employing a multiple hypotheses approach.
  • To enable flexible integration with various sensor types and simultaneous estimation of structure and motion.

Main Methods:

  • Developed a scene flow estimation algorithm utilizing multiple hypotheses to resolve motion ambiguities.
  • Implemented temporal information propagation for robust ambiguity resolution across frames.
  • Explored methods for motion field smoothing without compromising the benefits of multiple hypotheses.
  • Incorporated a probabilistic approach for occlusion estimation.
  • Utilized a data-driven tracking approach for 3D trajectory estimation.

Main Results:

  • The algorithm achieves orders of magnitude faster performance compared to existing techniques.
  • Demonstrated significant performance improvements on benchmark data, surpassing previous state-of-the-art results.
  • Achieved 10% and 15% performance gains through probabilistic occlusion estimation and motion field smoothing.
  • Successfully estimated 3D hand trajectories for sign language recognition without complex appearance modeling.

Conclusions:

  • The proposed scene flow algorithm offers a computationally efficient and accurate solution for 3D motion estimation.
  • The multiple hypotheses approach effectively handles motion ambiguities and avoids oversmoothing.
  • The algorithm's flexibility with sensor inputs and temporal propagation enhances its applicability in real-world scenarios.
  • This work advances 3D motion analysis, with potential applications in robotics, augmented reality, and human-computer interaction.