Answers.Body: <p>I think, that the answer depends on the application, but a possible choice would be store it as a <code>mesh</code> - a list of vertices <span class="math-container">$V$</span> and edges <span class="math-container">$E$</span>. Instead of edges, one can work with polygons, and define connectivity <span class="math-container">$F$</span> - for triplets of vertices <span class="math-container">$(v_i, v_j, v_k)$</span>.</p>
  <p>There <a href="https://ranahanocka.github.io/MeshCNN/" rel="nofollow noreferrer">is a nice paper</a> on Mesh CNN that can handle various geometric object.</p>
  <p>For the special case of boxes maybe there is a more educated approach, but since you would like to handle later more complex shapes, I would suggest to work with this architecture from the start.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-16T05:19:37.660Z
Answers.Text: Very nice paper but the methodology isn't suitable for my workflow. I just updated my post to include more info on what I want the NN to look for.
Answers.UserDisplayName: Julia_arch
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Answers.CreationDate: 2021-09-14T14:41:49.080Z
Answers.LastActivityDate: 2021-09-14T14:41:49.080Z
Answers.UserDisplayName: spiridon_the_sun_rotator
Body: <p>How do I prepare the info of 3D models to use with NN? For example, I have thousands of models with boxes similar to the ones in the image below. I can extract the vertices and their normals that make up the faces of these boxes. Similarly, I would like to prepare the info of the red-shaded surfaces, again I have their vertices and their normals. For future studies, I will have more complex shapes such as cylinders, pyramids,...etc. What would be the best way to represent these complex shapes for NN?</p>
  <p>Update: These boxes don't stay in the same position, see the second image I added. I will have different geometric models and different red-shaded areas on the surfaces of these objects. The NN output would be a number for each surface of these boxes/objects. The number represents the surface temperature. The input would be the following:</p>
  <p>1- Some climate information such as (air temperature, humidity, ...etc.)
  3- The location and size of the buildings that are represented in boxes/or maybe other shapes.
  4- The size and the location of the red-shaded areas (red-shaded areas represent the shadow cast by buildings.
  5- Material of each surface (concrete, brick,...etc).</p>
  <p><a href="https://i.sstatic.net/8CfqT.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/8CfqT.png" alt="enter image description here" /></a></p>
  <p><a href="https://i.sstatic.net/DvH7P.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/DvH7P.png" alt="enter image description here" /></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-14T05:07:17.463Z
Comments.Text: Why not as a tensor ?
Comments.UserDisplayName: hanugm
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Comments.CreationDate: 2021-09-14T08:59:59.690Z
Comments.Text: What do you want the NN to look at? A picture is usually treated as pixels; correspondingly a 3D picture could be treated as voxels.
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-14T09:00:31.077Z
Comments.Text: @hanugm what data goes in the tensor?
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-14T09:44:29.990Z
Comments.Text: Intensity values in 3D simulation as you told. @user253751
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-14T09:48:53.057Z
Comments.Text: the advantage of voxels is that they are equivalent to pixels, which we know work well. The *disadvantage* is that they're a lot of data! It would be convenient if the vertex coordinates etc could somehow be passed to a NN but I can't imagine an NN learning to work with that input.
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-16T05:17:44.727Z
Comments.Text: I just updated my post to include more info
Comments.UserDisplayName: Julia_arch
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-10-15T14:39:33.960Z
Comments.Text: I don't think NNs are suitable to handle this kind of data. As @spiridon_the_sun_rotator mention mesh CNNs, GNNs, pointnet are more appropriate for this task. As you mention, the methodology is not suitable for your task, I don't understand why? You can use the aforementioned feature vector in mesh CNN.
Comments.UserDisplayName: Swakshar Deb
Tags: <neural-networks><machine-learning><vectors>
Title: How do I prepare this 3D data for NN?
UserDisplayName: Julia_arch
ThreadText: # How do I prepare this 3D data for NN?
  
  (Asked by: username_0 on 2021-09-14)
  
  How do I prepare the info of 3D models to use with NN? For example, I have thousands of models with boxes similar to the ones in the image below. I can extract the vertices and their normals that make up the faces of these boxes. Similarly, I would like to prepare the info of the red-shaded surfaces, again I have their vertices and their normals. For future studies, I will have more complex shapes such as cylinders, pyramids,...etc. What would be the best way to represent these complex shapes for NN?
  
  Update: These boxes don't stay in the same position, see the second image I added. I will have different geometric models and different red-shaded areas on the surfaces of these objects. The NN output would be a number for each surface of these boxes/objects. The number represents the surface temperature. The input would be the following:
  
  1- Some climate information such as (air temperature, humidity, ...etc.)
  3- The location and size of the buildings that are represented in boxes/or maybe other shapes.
  4- The size and the location of the red-shaded areas (red-shaded areas represent the shadow cast by buildings.
  5- Material of each surface (concrete, brick,...etc).
  
  [![enter image description here](https://i.sstatic.net/8CfqT.png)](https://i.sstatic.net/8CfqT.png)
  
  [![enter image description here](https://i.sstatic.net/DvH7P.png)](https://i.sstatic.net/DvH7P.png)
  
  **Tags:** neural-networks, machine-learning, vectors
  > **Comments:**
  > **username_1**: Why not as a tensor ?  
  > **username_2**: What do you want the NN to look at? A picture is usually treated as pixels; correspondingly a 3D picture could be treated as voxels.  
  > **username_2**: @hanugm what data goes in the tensor?  
  > **username_1**: Intensity values in 3D simulation as you told. @user253751  
  > **username_2**: the advantage of voxels is that they are equivalent to pixels, which we know work well. The *disadvantage* is that they're a lot of data! It would be convenient if the vertex coordinates etc could somehow be passed to a NN but I can't imagine an NN learning to work with that input.  
  > **username_0**: I just updated my post to include more info  
  > **username_3**: I don't think NNs are suitable to handle this kind of data. As @spiridon_the_sun_rotator mention mesh CNNs, GNNs, pointnet are more appropriate for this task. As you mention, the methodology is not suitable for your task, I don't understand why? You can use the aforementioned feature vector in mesh CNN.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_4 on 2021-09-14. Score: 2
  
  I think, that the answer depends on the application, but a possible choice would be store it as a `mesh` - a list of vertices $V$ and edges $E$. Instead of edges, one can work with polygons, and define connectivity $F$ - for triplets of vertices $(v\_i, v\_j, v\_k)$.
  
  There [is a nice paper](https://ranahanocka.github.io/MeshCNN/) on Mesh CNN that can handle various geometric object.
  
  For the special case of boxes maybe there is a more educated approach, but since you would like to handle later more complex shapes, I would suggest to work with this architecture from the start.
  
  > **Comments:**
  > **username_0**: Very nice paper but the methodology isn't suitable for my workflow. I just updated my post to include more info on what I want the NN to look for.  
  
  ---
  
  