scholarly journals A 3D Shape Recognition Method Using Hybrid Deep Learning Network CNN–SVM

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 649
Author(s):  
Long Hoang ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
3D Model ◽  
Shape Recognition ◽  
Point Clouds ◽  
Support Vector ◽  
Learning Network ◽  
3D Shape ◽  
3D Data ◽  
Deep Learning Network

3D shape recognition becomes necessary due to the popularity of 3D data resources. This paper aims to introduce the new method, hybrid deep learning network convolution neural network–support vector machine (CNN–SVM), for 3D recognition. The vertices of the 3D mesh are interpolated to be converted into Point Clouds; those Point Clouds are rotated for 3D data augmentation. We obtain and store the 2D projection of this 3D augmentation data in a 32 × 32 × 12 matrix, the input data of CNN–SVM. An eight-layer CNN is used as the algorithm for feature extraction, then SVM is applied for classifying feature extraction. Two big datasets, ModelNet40 and ModelNet10, of the 3D model are used for model validation. Based on our numerical experimental results, CNN–SVM is more accurate and efficient than other methods. The proposed method is 13.48% more accurate than the PointNet method in ModelNet10 and 8.5% more precise than 3D ShapeNets for ModelNet40. The proposed method works with both the 3D model in the augmented/virtual reality system and in the 3D Point Clouds, an output of the LIDAR sensor in autonomously driving cars.

scholarly journals Detection of Surface Defects in Logs Using Point Cloud Data and Deep Learning

2021 ◽  
Vol 15 ◽  
pp. 607-616
Author(s):  
Shengbo Liu ◽  
Pengyuan Fu ◽  
Lei Yan ◽  
Jian Wu ◽  
Yandong Zhao
Keyword(s):  
Deep Learning ◽  
Surface Defects ◽  
Recognition Rate ◽  
Point Clouds ◽  
Data Set ◽  
Cloud Data ◽  
Wood Processing ◽  
3D Point Clouds ◽  
3D Data ◽  
Deep Learning Network

Deep learning classification based on 3D point clouds has gained considerable research interest in recent years.The classification and quantitative analysis of wood defects are of great significance to the wood processing industry. In order to solve the problems of slow processing and low robustness of 3D data. This paper proposes an improvement based on littlepoint CNN lightweight deep learning network, adding BN layer. And based on the data set made by ourselves, the test is carried out. The new network bnlittlepoint CNN has been improved in speed and recognition rate. The correct rate of recognition for non defect log, non defect log and defect log as well as defect knot and dead knot can reach 95.6%.Finally, the "dead knot" and "loose knot" are quantitatively analyzed based on the "integral" idea, and the volume and surface area of the defect are obtained to a certain extent,the error is not more than 1.5% and the defect surface reconstruction is completed based on the triangulation idea.

scholarly journals Orientation-Encoding CNN for Point Cloud Classification and Segmentation

2021 ◽  
Vol 3 (3) ◽  
pp. 601-614
Author(s):  
Hongbin Lin ◽  
Wu Zheng ◽  
Xiuping Peng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Feature Learning ◽  
Point Clouds ◽  
Point Sets ◽  
Learning Network ◽  
Rule Structure ◽  
Visual Tasks ◽  
Deep Learning Network ◽  
Point Cloud Classification

With the introduction of effective and general deep learning network frameworks, deep learning based methods have achieved remarkable success in various visual tasks. However, there are still tough challenges in applying them to convolutional neural networks due to the lack of a potential rule structure of point clouds. Therefore, by taking the original point clouds as the input data, this paper proposes an orientation-encoding (OE) convolutional module and designs a convolutional neural network for effectively extracting local geometric features of point sets. By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. Further experiments on diverse datasets show that the proposed method has competitive performance on classification and segmentation tasks of point sets.

PGNet: Progressive Feature Guide Learning Network for Three-dimensional Shape Recognition

2021 ◽  
Vol 17 (3) ◽  
pp. 1-17
Author(s):  
Jie Nie ◽  
Zhi-Qiang Wei ◽  
Weizhi Nie ◽  
An-An Liu
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Visual Information ◽  
Feature Fusion ◽  
Shape Representation ◽  
Shape Recognition ◽  
Three Dimensional ◽  
Learning Network ◽  
3D Shape ◽  
Fusion Scheme

Three-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.

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