Journal ArticleDOI
Latent-MVCNN: 3D Shape Recognition Using Multiple Views from Pre-defined or Random Viewpoints
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TLDR
The experimental results show that the LMVCNN achieves competitive performance in 3D shape recognition on ModelNet10 and ModelNet40 for both the pre-defined and the random viewpoints and exhibits promising performance when the number of view-images is quite small.Abstract:
The Multi-view Convolution Neural Network (MVCNN) has achieved considerable success in 3D shape recognition. However, 3D shape recognition using view-images from random viewpoints has not been yet exploited in depth. In addition, 3D shape recognition using a small number of view-images remains difficult. To tackle these challenges, we developed a novel Multi-view Convolution Neural Network, “Latent-MVCNN” (LMVCNN), that recognizes 3D shapes using multiple view-images from pre-defined or random viewpoints. The LMVCNN consists of three types of sub Convolution Neural Networks. For each view-image, the first type of CNN outputs multiple category probability distributions and the second type of CNN outputs a latent vector to help the first type of CNN choose the decent distribution. The third type of CNN outputs the transition probabilities from the category probability distributions of one view to the category probability distributions of another view, which further helps the LMVCNN to find the decent category probability distributions for each pair of view-images. The three CNNs cooperate with each other to the obtain satisfactory classification scores. Our experimental results show that the LMVCNN achieves competitive performance in 3D shape recognition on ModelNet10 and ModelNet40 for both the pre-defined and the random viewpoints and exhibits promising performance when the number of view-images is quite small.read more
Citations
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Journal ArticleDOI
MHFP: Multi-view based hierarchical fusion pooling method for 3D shape recognition
TL;DR: Wang et al. as discussed by the authors proposed a multi-view based Hierarchical Fusion Pooling Method (MHFP) for 3D Model Recognition, which hierarchically fuses the features of multiview into a compact descriptor.
Journal ArticleDOI
PVLNet: Parameterized-View-Learning neural network for 3D shape recognition
TL;DR: A novel light-weight multi- view based network built on parameterized-view-learning mechanism, PVLNet, which can achieve the state-of-the-art performance with only 1/10 FLOPs compared with previous multi-view based methods is proposed.
Journal ArticleDOI
Fusion of a Static and Dynamic Convolutional Neural Network for Multiview 3D Point Cloud Classification
TL;DR: FSDCNet is a neural network model based on the fusion of static and dynamic convolution that builds a global attention pooling, integrating the most crucial information on different view features to the greatest extent, and can improve the classification accuracy of point cloud data.
Journal ArticleDOI
Automatic Representative View Selection of a 3D Cultural Relic Using Depth Variation Entropy and Depth Distribution Entropy
TL;DR: In this article, a canonical pose 3D cultural relic was generated using principal component analysis and a set of depth maps obtained by orthographic cameras was then captured on the dense vertices of a geodesic unit-sphere by subdividing the regular unit octahedron.
Journal ArticleDOI
LiSurveying: A high-resolution TLS-LiDAR benchmark
Gabriel Lugo,Ryan Li,Rutvik Chauhan,Palak Tiwary,Utkarsh Kumar Pandey,Archi Patel,Steve Rombough,Rod Schatz,Irene Cheng +8 more
TL;DR: LiSurveying as mentioned in this paper is a large-scale point-cloud dataset with over a billion points and uncommon urban object categories in complex outdoor environments, which can be used to evaluate 3D pointcloud classification, semantic segmentation, and object detection algorithms.
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