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ELF-Nets: Deep learning on point clouds using extended laplacian filter
Seon Ho Lee
, Han Ul Kim
,
Chang Su Kim
*
*
Corresponding author for this work
School of Electrical Engineering
Center for Artificial Intelligence Research
Research output
:
Contribution to journal
›
Article
›
peer-review
8
Citations (Scopus)
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Dive into the research topics of 'ELF-Nets: Deep learning on point clouds using extended laplacian filter'. Together they form a unique fingerprint.
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Keyphrases
2D Image Processing
10%
3D Shape
10%
3D Vision
10%
Convolutional Layer
10%
Deep Learning
10%
Deep Learning Framework
10%
Deep Learning on Point Cloud
100%
Design Principles
10%
Discrete Laplacian
10%
Feature Extraction
10%
Filter Convolution
10%
Filter Matrix
10%
Fully Connected Layer
10%
Laplacian Filter
100%
Neighboring Points
20%
Object Classification
10%
Object Parts
10%
Part Segmentation
10%
Point Cloud
40%
Scalar
10%
Segmentation Problem
10%
State Filter
10%
State-of-the-art Techniques
10%
Two-state
10%
Vision Task
10%
Weight Function
10%
Computer Science
Convolution Layer
10%
Deep Learning Method
100%
Experimental Result
10%
Feature Extraction
10%
Fully Connected Layer
10%
Image Processing
10%
Laplacian Filter
100%
Learning Framework
10%
Point Cloud
100%
Relative Importance
10%
Segmentation Task
10%
Weighting Functions
10%
Engineering
Deep Learning Method
100%
Experimental Result
10%
Feature Extraction
10%
Filter State
10%
Image Processing
10%
Laplacian Filter
100%
Point Cloud
100%
Relative Importance
10%
Set Point
10%
State-of-the-Art Technique
10%
Mathematics
3D Shape
10%
Center Point
10%
Connected Layer
10%
Convolution
20%
Deep Learning Method
100%
Image Processing
10%
Laplace Operator
100%
Matrix (Mathematics)
10%
Relative Importance
10%
Set Point
10%
Weighting Functions
10%
Chemical Engineering
Deep Learning Method
100%