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

Upsampling01:22

Upsampling

246
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
246
Computed Tomography01:10

Computed Tomography

4.6K
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...
4.6K
Downsampling01:20

Downsampling

169
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
169
Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Survivin, the promising target in hepatocellular carcinoma gene therapy.

Cancer biology & therapy·2008
Same author

Curcumin protects dopaminergic neuron against LPS induced neurotoxicity in primary rat neuron/glia culture.

Neurochemical research·2008
Same author

Cellular mechanisms of reduced sarcoplasmic reticulum Ca2+ content in L-thyroxin induced rat ventricular hypertrophy.

Acta pharmacologica Sinica·2008
Same author

Promoting the formation and stabilization of G-quadruplex by dinuclear RuII complex Ru2(obip)L4.

Inorganic chemistry·2008
Same author

Identification of direct target genes using joint sequence and expression likelihood with application to DAF-16.

PloS one·2008
Same author

In vitro and in vivo investigations on the antiviral activity of a series of mixed-valence rare earth borotungstate heteropoly blues.

European journal of medicinal chemistry·2008

Related Experiment Video

Updated: Jul 14, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K

TUCNet: A channel and spatial attention-based graph convolutional network for teeth upsampling and completion.

Mengting Liu1, Xiaojie Li2, Jie Liu1

  • 1School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China.

Computers in Biology and Medicine
|October 6, 2023
PubMed
Summary
This summary is machine-generated.

TUCNet effectively completes sparse 3D dental models by intelligently filling missing data. This novel method enhances the quality of 3D teeth point clouds for improved dental applications.

Keywords:
Channel and spatial attentionPoint cloud completionTeeth point cloud completionUpsample transformer

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

439

Related Experiment Videos

Last Updated: Jul 14, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

439

Area of Science:

  • Computer Vision
  • Medical Imaging
  • Dental Technology

Background:

  • 3D scanning is increasingly used in dentistry for oral cavity capture.
  • Sparse and incomplete point cloud data from 3D scans pose challenges for dental applications.
  • High-quality 3D dental models are crucial for prosthodontics and orthodontics.

Purpose of the Study:

  • To develop a high-resolution teeth point cloud completion method (TUCNet).
  • To address the issue of missing information in sparse oral point cloud data.
  • To generate dense and complete 3D teeth point clouds from incomplete scans.

Main Methods:

  • Proposed Channel and Spatial Attentive EdgeConv (CSAE) module for fusing local and global point features.
  • Developed a CSAE-based point cloud upsample (CPCU) module for gradual point cloud densification.
  • Employed a tree-based approach with skip connections for hierarchical point cloud generation.

Main Results:

  • TUCNet achieved state-of-the-art performance on a dedicated teeth point cloud completion dataset.
  • The method demonstrated excellent performance on the general PCN dataset.
  • Experimental results validate the effectiveness of TUCNet in generating complete point clouds.

Conclusions:

  • TUCNet successfully completes sparse 3D dental models, producing high-resolution and dense point clouds.
  • The proposed CSAE and CPCU modules are effective for feature extraction and point cloud upsampling.
  • This method offers a significant advancement for 3D data processing in dental health applications.