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

Computed Tomography01:10

Computed Tomography

6.3K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
6.3K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

52
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
52
Reducing Line Loss01:18

Reducing Line Loss

194
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
194
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

260
Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
260

You might also read

Related Articles

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

Sort by
Same author

Novel rhino-like SHJH <sup></sup> mice with thyroid dysfunction.

Zoological research·2021
Same author

Accuracy of FibroTouch in assessing liver steatosis and fibrosis in patients with metabolic-associated fatty liver disease combined with type 2 diabetes mellitus.

Annals of palliative medicine·2021
Same author

H<sub>2</sub>O<sub>2</sub>-Mediated Oxidative Stress Enhances Cystathionine γ-Lyase-Derived H<sub>2</sub>S Synthesis via a Sulfenic Acid Intermediate.

Antioxidants (Basel, Switzerland)·2021
Same author

Cerebrospinal Fluid MicroRNA Changes in Cognitively Normal Veterans With a History of Deployment-Associated Mild Traumatic Brain Injury.

Frontiers in neuroscience·2021
Same author

Effects of fertilizer under different dripline spacings on summer maize in northern China.

Scientific reports·2021
Same author

RGD-functionalised melanin nanoparticles for intraoperative photoacoustic imaging-guided breast cancer surgery.

European journal of nuclear medicine and molecular imaging·2021
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
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes
05:19

Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes

Published on: April 5, 2024

2.4K

Deep Learning-Based Point Cloud Compression: An In-Depth Survey and Benchmark.

Wei Gao, Liang Xie, Songlin Fan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 31, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Deep learning significantly enhances point cloud compression (PCC), overcoming limitations of traditional methods. This review systematically analyzes deep PCC algorithms, datasets, and standards, identifying future research directions for efficient 3D data handling.

    More Related Videos

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
    09:19

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

    Published on: April 18, 2025

    818
    Using Computer Vision Libraries to Streamline Nuclei Quantification
    06:25

    Using Computer Vision Libraries to Streamline Nuclei Quantification

    Published on: June 6, 2025

    421

    Related Experiment Videos

    Last Updated: Sep 13, 2025

    Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes
    05:19

    Author Spotlight: Advancing Understanding of Age-Related Lens Stiffness Changes

    Published on: April 5, 2024

    2.4K
    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
    09:19

    Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

    Published on: April 18, 2025

    818
    Using Computer Vision Libraries to Streamline Nuclei Quantification
    06:25

    Using Computer Vision Libraries to Streamline Nuclei Quantification

    Published on: June 6, 2025

    421

    Area of Science:

    • Computer Vision
    • Data Compression
    • Machine Learning

    Background:

    • Explosive growth in 3D point cloud data strains storage and transmission.
    • Traditional hybrid point cloud compression (PCC) methods struggle with performance limitations.
    • Deep learning-based PCC offers a promising solution to enhance compression efficiency.

    Purpose of the Study:

    • To provide a systematic review of deep learning-based point cloud compression (PCC).
    • To analyze algorithm evolution, datasets, and international standards in deep PCC.
    • To identify challenges and future research directions in the field.

    Main Methods:

    • Review of popular point cloud datasets and their properties.
    • Analysis of algorithm evolution for both lossy and lossless deep PCC.
    • Investigation of MPEG and JPEG standards relevant to PCC.
    • Experimental benchmarking of representative deep PCC methods on multiple datasets.

    Main Results:

    • Comprehensive comparison of existing deep PCC methods, highlighting their strengths and weaknesses.
    • Identification of key trends and challenges in the current deep PCC landscape.
    • Empirical validation of the effectiveness of various deep learning approaches for PCC.

    Conclusions:

    • Deep learning is crucial for advancing point cloud compression.
    • A systematic understanding of deep PCC is needed to guide future research.
    • Future work should focus on addressing identified challenges for more efficient 3D data compression.