scholarly journals Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning From Unlabeled Videos and 3D-Shape Retrieval

2019 ◽  
Vol 28 (1) ◽  
pp. 279-290 ◽  
Author(s):  
Nenad Markus ◽  
Igor Pandzic ◽  
Jorgen Ahlberg
Keyword(s):  
Shape Retrieval ◽  
3D Shape Retrieval ◽  
Local Descriptors ◽  
3D Shape ◽  
Face Matching

Towards Fine-Grained Sketch-Based 3D Shape Retrieval

2021 ◽  
pp. 1-1
Author(s):  
Anran Qi ◽  
Yulia Gryaditskaya ◽  
Jifei Song ◽  
Yongxin Yang ◽  
Yonggang Qi ◽  
...  
Keyword(s):  
Shape Retrieval ◽  
3D Shape Retrieval ◽  
3D Shape ◽  
Fine Grained

scholarly journals Partial 3D Shape Retrieval by Reeb Pattern Unfolding

2009 ◽  
Vol 28 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Julien Tierny ◽  
Jean-Philippe Vandeborre ◽  
Mohamed Daoudi
Keyword(s):  
Shape Retrieval ◽  
3D Shape Retrieval ◽  
3D Shape

scholarly journals MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval

2019 ◽  
Vol 33 ◽  
pp. 8513-8520 ◽  
Author(s):  
Jianwen Jiang ◽  
Di Bao ◽  
Ziqiang Chen ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Shape Representation ◽  
The State ◽  
Shape Retrieval ◽  
3D Shape Retrieval ◽  
3D Shape ◽  
Loop Level ◽  
Art Methods

3D shape retrieval has attracted much attention and become a hot topic in computer vision field recently.With the development of deep learning, 3D shape retrieval has also made great progress and many view-based methods have been introduced in recent years. However, how to represent 3D shapes better is still a challenging problem. At the same time, the intrinsic hierarchical associations among views still have not been well utilized. In order to tackle these problems, in this paper, we propose a multi-loop-view convolutional neural network (MLVCNN) framework for 3D shape retrieval. In this method, multiple groups of views are extracted from different loop directions first. Given these multiple loop views, the proposed MLVCNN framework introduces a hierarchical view-loop-shape architecture, i.e., the view level, the loop level, and the shape level, to conduct 3D shape representation from different scales. In the view-level, a convolutional neural network is first trained to extract view features. Then, the proposed Loop Normalization and LSTM are utilized for each loop of view to generate the loop-level features, which considering the intrinsic associations of the different views in the same loop. Finally, all the loop-level descriptors are combined into a shape-level descriptor for 3D shape representation, which is used for 3D shape retrieval. Our proposed method has been evaluated on the public 3D shape benchmark, i.e., ModelNet40. Experiments and comparisons with the state-of-the-art methods show that the proposed MLVCNN method can achieve significant performance improvement on 3D shape retrieval tasks. Our MLVCNN outperforms the state-of-the-art methods by the mAP of 4.84% in 3D shape retrieval task. We have also evaluated the performance of the proposed method on the 3D shape classification task where MLVCNN also achieves superior performance compared with recent methods.

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