scholarly journals Joint Multi-view 2D Convolutional Neural Networks for 3D Object Classification

2020 ◽  
Author(s):  
Jinglin Xu ◽  
Xiangsen Zhang ◽  
Wenbin Li ◽  
Xinwang Liu ◽  
Junwei Han
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Computational Cost ◽  
Three Dimensional ◽  
Object Classification ◽  
Autonomous Driving ◽  
Outer Product ◽  
3D Object ◽  
Localization And Mapping ◽  
3D Object Classification

Three-dimensional (3D) object classification is widely involved in various computer vision applications, e.g., autonomous driving, simultaneous localization and mapping, which has attracted lots of attention in the committee. However, solving 3D object classification by directly employing the 3D convolutional neural networks (CNNs) generally suffers from high computational cost. Besides, existing view-based methods cannot better explore the content relationships between views. To this end, this work proposes a novel multi-view framework by jointly using multiple 2D-CNNs to capture discriminative information with relationships as well as a new multi-view loss fusion strategy, in an end-to-end manner. Specifically, we utilize multiple 2D views of a 3D object as input and integrate the intra-view and inter-view information of each view through the view-specific 2D-CNN and a series of modules (outer product, view pair pooling, 1D convolution, and fully connected transformation). Furthermore, we design a novel view ensemble mechanism that selects several discriminative and informative views to jointly infer the category of a 3D object. Extensive experiments demonstrate that the proposed method is able to outperform current state-of-the-art methods on 3D object classification. More importantly, this work provides a new way to improve 3D object classification from the perspective of fully utilizing well-established 2D-CNNs.

scholarly journals VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6735 ◽  
Author(s):  
Zishu Liu ◽  
Wei Song ◽  
Yifei Tian ◽  
Sumi Ji ◽  
Yunsick Sung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Object Classification ◽  
Point Clouds ◽  
Learning System ◽  
Learning Networks ◽  
3D Object ◽  
Feature Extractor ◽  
Irregular Point ◽  
3D Object Classification

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.

Invariant Gaussian–Hermite Moments Based Neural Networks for 3D Object Classification

2020 ◽  
Vol 30 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Amal Zouhri ◽  
Hicham Amakdouf ◽  
Mostafa El Mallahi ◽  
Ahmed Tahiri ◽  
Zakia Lakhliai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Object Classification ◽  
3D Object ◽  
3D Object Classification

scholarly journals Hypergraph Wavelet Neural Networks for 3D Object Classification

2021 ◽  
Author(s):  
Liping Nong ◽  
Junyi Wang ◽  
Jiming Lin ◽  
Hongbing Qiu ◽  
Lin Zheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Object Classification ◽  
Wavelet Neural Networks ◽  
3D Object ◽  
3D Object Classification

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

scholarly journals Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2801
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Three Dimensional ◽  
Mode Shapes ◽  
Identification Algorithm ◽  
Material Degradation ◽  
Local Material ◽  
Noisy Input ◽  
Shape Vector

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

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