Corpus ID: 27973832

Wide and deep volumetric residual networks for volumetric image classification

  title={Wide and deep volumetric residual networks for volumetric image classification},
  author={Varun Arvind and A. B. Costa and M. Badgeley and Samuel Cho and E. Oermann},
3D shape models that directly classify objects from 3D information have become more widely implementable. Current state of the art models rely on deep convolutional and inception models that are resource intensive. Residual neural networks have been demonstrated to be easier to optimize and do not suffer from vanishing/exploding gradients observed in deep networks. Here we implement a residual neural network for 3D object classification of the 3D Princeton ModelNet dataset. Further, we show… Expand
13 Citations
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  • 1
  • PDF
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  • 39
View-based weight network for 3D object recognition
  • Qiang Huang, Yongxiong Wang, Zhong Yin
  • Computer Science
  • Image Vis. Comput.
  • 2020
  • 2
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  • 27
  • PDF
A Multi-view Images Classification Based on Deep Graph Convolution
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  • 17
CurvMaps: A Novel Feature for 3D Model Classification


PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
  • 3,722
  • PDF
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
  • Daniel Maturana, S. Scherer
  • Computer Science
  • 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2015
  • 1,575
  • PDF
3D ShapeNets: A deep representation for volumetric shapes
  • 2,200
  • PDF
Deep Residual Learning for Image Recognition
  • 62,797
  • PDF
Going deeper with convolutions
  • 22,898
  • PDF
Wide Residual Networks
  • 2,874
  • PDF
Xception: Deep Learning with Depthwise Separable Convolutions
  • François Chollet
  • Computer Science, Mathematics
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
  • 3,997
  • PDF
Understanding how image quality affects deep neural networks
  • Samuel F. Dodge, Lina Karam
  • Computer Science
  • 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX)
  • 2016
  • 315
  • PDF