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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.0K
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.
1.0K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

137
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...
137
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

186
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...
186
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.9K
Deconvolution01:20

Deconvolution

271
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...
271

You might also read

Related Articles

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

Sort by
Same author

Multi-scale and context-aware enhanced YOLOv8 for breast tumor detection in ultrasound images.

Scientific reports·2026
Same author

Size-dependent lymphatic delivery of Gd-based MRI contrast agents: insights from <i>in vivo</i> studies.

Journal of materials chemistry. B·2026
Same author

NaGdF<sub>4</sub> Nanoprobe-Enhanced Multiparametric MRI for Evaluating Diabetes Mellitus-Associated Hepatic Dysfunction and Precancerous Biliary Transformation.

ACS applied materials & interfaces·2026
Same author

Combined Target-Immobilized and Library-Immobilized SELEX for Selecting High-Affinity α-Amanitin Aptamers.

Toxins·2026
Same author

A synergistic multiparametric MRI strategy for FAPα-targeted tumor fibrosis based on NaGdF<sub>4</sub>@PEG-FAPI nanoprobes.

Nanoscale·2026
Same author

Interpreting tissue stiffening with lung tumorigenesis by imaging architectural resembling of extracellular matrix components.

Communications biology·2026

Related Experiment Video

Updated: Sep 29, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K

DRI-MVSNet: A depth residual inference network for multi-view stereo images.

Ying Li1,2, Wenyue Li2,3, Zhijie Zhao2,3

  • 1College of Computer Science and Technology, Jilin University, Changchun, China.

Plos One
|March 23, 2022
PubMed
Summary

This study introduces DRI-MVSNet, a novel network for accurate 3D image reconstruction. It overcomes memory limitations to achieve superior point cloud accuracy and completeness compared to existing methods.

More Related Videos

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

665
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.2K

Related Experiment Videos

Last Updated: Sep 29, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
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

665
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.2K

Area of Science:

  • Computer Vision
  • 3D Reconstruction

Background:

  • Accurate 3D reconstruction is crucial for scene geometry restoration.
  • Current methods struggle with memory demands, leading to inaccuracies.
  • High-accuracy 3D scene reconstruction remains a significant challenge.

Purpose of the Study:

  • To propose a novel network, DRI-MVSNet, for highly accurate 3D image reconstruction.
  • To address memory constraints and improve the accuracy and completeness of reconstructed point clouds.

Main Methods:

  • A cascaded depth residual inference network (DRI-MVSNet) is proposed.
  • Utilizes a cross-view similarity-based feature map fusion module for residual inference.
  • Incorporates a combined module for channel and spatial information processing, attention mechanisms, and residual prediction with non-uniform depth sampling.

Main Results:

  • DRI-MVSNet demonstrates competitive performance on the DTU and Tanks & Temples datasets.
  • Achieves significantly superior accuracy and completeness in reconstructed point clouds compared to state-of-the-art benchmarks.
  • The proposed network effectively handles memory demands for improved 3D reconstruction.

Conclusions:

  • DRI-MVSNet offers a significant advancement in 3D image reconstruction.
  • The network's novel modules enhance feature representation and depth map generation.
  • It provides a robust solution for accurate and complete 3D scene reconstruction.