Answers.Body: <p>There are many approaches for training CNN on 3d data, but the decision to use a particular architecture is heavily dependant upon the format of your dataset.</p>
  
  <p>If you are using 3d point cloud data, I would suggest you go through <a href="https://arxiv.org/abs/1612.00593" rel="nofollow noreferrer">PointNet</a> and <a href="https://github.com/yangyanli/PointCNN" rel="nofollow noreferrer">PointCNN</a>.</p>
  
  <p>But training a CNN on 3d point clouds is very tough.</p>
  
  <p>There is also a way to train CNNs by posing the 3d structure from different viewpoints (<a href="https://arxiv.org/pdf/1505.00880.pdf" rel="nofollow noreferrer">Multiview CNNs</a>).</p>
  
  <p>But remember that training CNN on 3d data is really a tough task. </p>
  
  <p>If you plan to use a voxelized input data format, I suggest going through <a href="https://arxiv.org/abs/1711.06396" rel="nofollow noreferrer">VoxelNet</a>.</p>
  
  <p>Since you are mentioning deconvolution, the most relevant paper I can come across is <a href="https://arxiv.org/abs/1606.06650" rel="nofollow noreferrer">3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation</a>.</p>
  
  <p>But deconvolution in its own right is a very expensive operation, which acting on 3d data makes it very hard, so I would suggest you check for alternate methods.</p>
  
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Answers.Text: why is it difficult to learn on from 3D data?
Answers.UserDisplayName: Charlie Parker
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Answers.Text: Thanks! And for the last sentence, I understand the 3d convolution is a memory consuming operation. Is it because of that that you say it is hard. And also what is the alternate methods except optimization methods like RLTV and etc. ? Thanks again
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Answers.Text: yes , deconvolution is more consuming than convolution due to upsampling involved in it . regarding optimization methods i don't know much. PS: can you expand RLTV so that i can check?
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Body: <p>I am trying to do <strong>3d image deconvolution</strong> using <strong>convolution neural network</strong>. But I cannot find many famous CNNs that perform a 3d convolution. Can anyone point out some for me?</p>
  <p>Background: I am using PyTorch, but any language is OK. What I want to know most is the network structure. I can't find papers on this topic.</p>
  <p>Links to research papers would be especially appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-12T03:54:27.720Z
Comments.Text: See [Learning Descriptor Networks for 3D Shape Synthesis and Analysis](https://arxiv.org/abs/1804.00586).
Comments.UserDisplayName: Kicr
Tags: <convolutional-neural-networks><reference-request><model-request><3d-convolution>
Title: Which neural network architectures are there that perform 3D convolutions?
UserDisplayName: Kicr
ThreadText: # Which neural network architectures are there that perform 3D convolutions?
  
  (Asked by: username_0 on 2018-04-10)
  
  I am trying to do **3d image deconvolution** using **convolution neural network**. But I cannot find many famous CNNs that perform a 3d convolution. Can anyone point out some for me?
  
  Background: I am using PyTorch, but any language is OK. What I want to know most is the network structure. I can't find papers on this topic.
  
  Links to research papers would be especially appreciated.
  
  **Tags:** convolutional-neural-networks, reference-request, model-request, 3d-convolution
  > **Comments:**
  > **username_0**: See [Learning Descriptor Networks for 3D Shape Synthesis and Analysis](https://arxiv.org/abs/1804.00586).  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-05-29. Score: 1 | Accepted answer
  
  There are many approaches for training CNN on 3d data, but the decision to use a particular architecture is heavily dependant upon the format of your dataset.
  
  If you are using 3d point cloud data, I would suggest you go through [PointNet](https://arxiv.org/abs/1612.00593) and [PointCNN](https://github.com/yangyanli/PointCNN).
  
  But training a CNN on 3d point clouds is very tough.
  
  There is also a way to train CNNs by posing the 3d structure from different viewpoints ([Multiview CNNs](https://arxiv.org/pdf/1505.00880.pdf)).
  
  But remember that training CNN on 3d data is really a tough task.
  
  If you plan to use a voxelized input data format, I suggest going through [VoxelNet](https://arxiv.org/abs/1711.06396).
  
  Since you are mentioning deconvolution, the most relevant paper I can come across is [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://arxiv.org/abs/1606.06650).
  
  But deconvolution in its own right is a very expensive operation, which acting on 3d data makes it very hard, so I would suggest you check for alternate methods.
  
  > **Comments:**
  > **username_0**: Thanks! And for the last sentence, I understand the 3d convolution is a memory consuming operation. Is it because of that that you say it is hard. And also what is the alternate methods except optimization methods like RLTV and etc. ? Thanks again  
  > **username_1**: yes , deconvolution is more consuming than convolution due to upsampling involved in it . regarding optimization methods i don't know much. PS: can you expand RLTV so that i can check?  
  > **username_2**: why is it difficult to learn on from 3D data?  
  
  ---
  
  