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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2018.2848628
|2 doi
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|a eng
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| 100 |
1 |
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|a Shu, Zhenyu
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder
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| 264 |
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|c 2019
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Revised 23.07.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Considering the fact that points of interest on 3D shapes can be discriminated from a geometric perspective, it is reasonable to map the geometric signature of a point $p$p to a probability value encoding to what degree $p$p is a point of interest, especially for a specific class of 3D shapes. Based on the observation, we propose a three-phase algorithm for learning and predicting points of interest on 3D shapes by using multiple feature descriptors. Our algorithm requires two separate deep neural networks (stacked auto-encoders) to accomplish the task. During the first phase, we predict the membership of the given 3D shape according to a set of geometric descriptors using a deep neural network. After that, we train the other deep neural network to predict a probability distribution defined on the surface representing the possibility of a point being a point of interest. Finally, we use a manifold clustering technique to extract a set of points of interest as the output. Experimental results show superior detection performance of the proposed method over the previous state-of-the-art approaches
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Xin, Shiqing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Ligang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kavan, Ladislav
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 25(2019), 8 vom: 18. Aug., Seite 2583-2596
|w (DE-627)NLM098269445
|x 1941-0506
|7 nnns
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| 773 |
1 |
8 |
|g volume:25
|g year:2019
|g number:8
|g day:18
|g month:08
|g pages:2583-2596
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|u http://dx.doi.org/10.1109/TVCG.2018.2848628
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