Answers.Body: <p>I'm working on a similar problem. I'm using a 2D point cloud of an object, for example, X and Y coordinates for height, and with that more simple data set I will train a regression model (currently working on that). In my opinion, this approach with dissecting complex point cloud into cross sections that contain wanted dimension and feeding that to the model will be more simple and easier for the training. </p>
  
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Answers.CreationDate: 2019-11-15T20:28:47.473Z
Answers.Text: I know that this doesn't answers your question directly, but I hope it helps and I don't have enough points to comment on your question.
Answers.UserDisplayName: Ermin Podrug
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Answers.CreationDate: 2019-11-15T19:12:41.487Z
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Answers.LastEditDate: 2019-11-15T20:30:05.400Z
Answers.UserDisplayName: Ermin Podrug
Body: <p>Is it possible to train a convolutional neural network (CNN) to predict the dimensions of primitive objects such as (spheres, cylinders, cuboids, etc.) from point clouds?</p>
  
  <p>The input to the CNN will be the point cloud of a <strong>single</strong> object and the output will be the dimensions of the object (for example, radius and height of the cylinder). The training data will be the point cloud of the object with the ground truth dimensions in a regression final layer? </p>
  
  <p>I think it is possible for images since it will similar to a bounding box detection, but I am not sure with point clouds.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-11-15T20:37:54.983Z
Comments.Text: How exactly do the points relate to the object? Are they merely all contained within the object? Are they only around the surface of the object? Do they define the surface of the object? Are they random/approximate, or exact?
Comments.UserDisplayName: The Guy with The Hat
Tags: <machine-learning><deep-learning><convolutional-neural-networks><object-recognition><object-detection>
Title: Is it possible to train a CNN to predict the dimensions of primitive objects from point clouds?
UserDisplayName: MostafaBakr
ThreadText: # Is it possible to train a CNN to predict the dimensions of primitive objects from point clouds?
  
  (Asked by: username_0 on 2019-11-15)
  
  Is it possible to train a convolutional neural network (CNN) to predict the dimensions of primitive objects such as (spheres, cylinders, cuboids, etc.) from point clouds?
  
  The input to the CNN will be the point cloud of a **single** object and the output will be the dimensions of the object (for example, radius and height of the cylinder). The training data will be the point cloud of the object with the ground truth dimensions in a regression final layer?
  
  I think it is possible for images since it will similar to a bounding box detection, but I am not sure with point clouds.
  
  **Tags:** machine-learning, deep-learning, convolutional-neural-networks, object-recognition, object-detection
  
  > **Comments:**
  > **username_1**: How exactly do the points relate to the object? Are they merely all contained within the object? Are they only around the surface of the object? Do they define the surface of the object? Are they random/approximate, or exact?  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2019-11-15. Score: 2
  
  I'm working on a similar problem. I'm using a 2D point cloud of an object, for example, X and Y coordinates for height, and with that more simple data set I will train a regression model (currently working on that). In my opinion, this approach with dissecting complex point cloud into cross sections that contain wanted dimension and feeding that to the model will be more simple and easier for the training.
  
  > **Comments:**
  > **username_2**: I know that this doesn't answers your question directly, but I hope it helps and I don't have enough points to comment on your question.  
  
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