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.

Introduction and Methods of Leveling

Introduction and Methods of Leveling

Leveling is a surveying procedure used to determine elevation differences between distant points. Elevation refers to the vertical distance above or below a reference datum, typically mean sea level (MSL). In the United States, elevations are often referenced to the mean sea level station at Father Point Rimouski along the St. Lawrence Seaway. To make the datum accessible, permanent markers are established throughout the region. These markers, called benchmarks, have known elevations. If the...

Deconvolution

Deconvolution

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...

You might also read

Related Articles

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

Sort by

Same author

MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Albedo Post-Processing.

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

Same author

DiWTBR: Dilated Wavelet Transformer for Efficient Megapixel Bokeh Rendering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

Same author

Micro-ring resonator assisted spiking neural network for efficient object detection.

Optics letters·2025

Same author

Lactylation of Histone H3k18 and Egr1 Promotes Endothelial Glycocalyx Degradation in Sepsis-Induced Acute Lung Injury.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024

Same author

Shenfu injection alleviate gut ischemia/reperfusion injury after severe hemorrhagic shock through improving intestinal microcirculation in rats.

Heliyon·2024

Same author

Development of an Intelligent Reactive Oxygen Species-Responsive Dual-Drug Delivery Nanoplatform for Enhanced Precise Therapy of Acute Lung Injury.

International journal of nanomedicine·2024

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

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

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

Published on: August 12, 2021

LNMVSNet: A Low-Noise Multi-View Stereo Depth Inference Method for 3D Reconstruction.

Weiming Luo¹, Zongqing Lu¹, Qingmin Liao¹

¹Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)

|April 27, 2024

Summary

This summary is machine-generated.

This study introduces LNMVSNet, a novel deep learning network for low-noise multi-view stereo (MVS) 3D reconstruction. LNMVSNet enhances feature attention and fusion, significantly improving accuracy and detail recovery in noisy conditions.

Keywords:

RGB 3D reconstruction depth estimation multi-view stereo

More Related Videos

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

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

Published on: December 3, 2013

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Related Experiment Videos

Last Updated: Jun 27, 2025

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

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

Published on: August 12, 2021

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

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

Published on: December 3, 2013

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Area of Science:

Computer Vision
3D Reconstruction
Deep Learning

Background:

Multi-view stereo (MVS) 3D reconstruction is widely used due to accessible RGB cameras.
Traditional and deep learning MVS methods struggle with noise, impacting model and depth map quality.
Noise, including multiplicative noise and negative gain, degrades MVS accuracy.

Purpose of the Study:

To develop a robust deep learning network for high-precision MVS 3D reconstruction.
To address the limitations of existing MVS methods in handling noisy image data.
To improve the accuracy and completeness of 3D models generated from RGB images.

Main Methods:

Introduced LNMVSNet, a deep learning network focusing on local feature attention.
Implemented multi-scale feature fusion within the network architecture.
Evaluated performance on multiple benchmark datasets for MVS tasks.

Main Results:

LNMVSNet demonstrated superior performance in low-noise MVS 3D reconstruction.
The network significantly improved reconstruction accuracy and completeness.
Enhanced recovery of fine details and clear feature delineation was observed.

Conclusions:

LNMVSNet effectively overcomes noise challenges in MVS 3D reconstruction.
The proposed method offers a promising solution for accurate and detailed 3D model generation.
Advancements in MVS have potential applications in industrial inspection and virtual environments.