Evolutionary 3D-Shape Segmentation Using Satellite Seeds

2012 ◽  
pp. 438-447
Author(s):  
Kai Engel ◽  
Heinrich Müller
Keyword(s):  
3D Shape ◽  
Shape Segmentation

scholarly journals Multi-layer Ontologies for Integrated 3D Shape Segmentation and Annotation

2016 ◽  
pp. 181-206 ◽  
Author(s):  
Thomas Dietenbeck ◽  
Fakhri Torkhani ◽  
Ahlem Othmani ◽  
Marco Attene ◽  
Jean-Marie Favreau
Keyword(s):  
3D Shape ◽  
Shape Segmentation

Pose-Consistent 3D Shape Segmentation Based on a Quantum Mechanical Feature Descriptor

2011 ◽  
pp. 122-131 ◽  
Author(s):  
Mathieu Aubry ◽  
Ulrich Schlickewei ◽  
Daniel Cremers
Keyword(s):  
Quantum Mechanical ◽  
Feature Descriptor ◽  
Mechanical Feature ◽  
3D Shape ◽  
Shape Segmentation

scholarly journals SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform

2020 ◽  
pp. 1-1
Author(s):  
Cheng Lin ◽  
Lingjie Liu ◽  
Changjian Li ◽  
Leif Kobbelt ◽  
Bin Wang ◽  
...  
Keyword(s):  
Medial Axis ◽  
Medial Axis Transform ◽  
3D Shape ◽  
Shape Segmentation

scholarly journals 3D Shape Segmentation with Projective Convolutional Networks

2017 ◽  
Author(s):  
Evangelos Kalogerakis ◽  
Melinos Averkiou ◽  
Subhransu Maji ◽  
Siddhartha Chaudhuri
Keyword(s):  
3D Shape ◽  
Convolutional Networks ◽  
Shape Segmentation

Directionally Convolutional Networks for 3D Shape Segmentation

2017 ◽  
Author(s):  
Haotian Xu ◽  
Ming Dong ◽  
Zichun Zhong
Keyword(s):  
3D Shape ◽  
Convolutional Networks ◽  
Shape Segmentation

Unsupervised 3D shape segmentation and co-segmentation via deep learning

2016 ◽  
Vol 43 ◽  
pp. 39-52 ◽  
Author(s):  
Zhenyu Shu ◽  
Chengwu Qi ◽  
Shiqing Xin ◽  
Chao Hu ◽  
Li Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Shape ◽  
Shape Segmentation

3D Shape Segmentation: A Review

2021 ◽  
Vol 15 ◽  
Author(s):  
Rui Li ◽  
Qingjin Peng
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Large Scale ◽  
Shape Features ◽  
3D Shape ◽  
Shape Segmentation ◽  
Machine Learning Methods ◽  
Scale Models ◽  
Pros And Cons ◽  
Identification Analysis

Background: Shape segmentation is commonly required in many engineering fields to separate a 3D shape into pieces for some specific applications. Although there are different methods proposed to segment the 3D shape, there is a lack of analyses of their efficiency and accuracy. It is a challenge to select an effective method to meet a particular requirement of the shape segmentation. Objective: This paper reviews existing methods of the shape segmentation to summarize the methods and processes to identify their pros and cons. Method: The process of the shape segmentation is summarized in two steps of the feature extraction and model separation. Results: Shape features are identified from the available methods. Different methods of the shape segmentation are evaluated. The challenge and trend of the shape segmentation are discussed. Conclusion: Clustering is the most used method for the shape segmentation. Machine learning methods are trend of 3D shape segmentations for identification, analysis and reconstruction of large-scale models.

Learning Manipulation Tasks from Human Demonstration and 3D Shape Segmentation

2012 ◽  
Vol 26 (16) ◽  
pp. 1863-1884 ◽  
Author(s):  
Jacopo Aleotti ◽  
Stefano Caselli
Keyword(s):  
3D Shape ◽  
Shape Segmentation ◽  
Human Demonstration
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