Answers.Body: <p>What you are looking for are adversial neural networks. GANs or other adversial neural networks are used to classify or detect objects that are partially occluded. They can perform very well given enough training data and tend be quite robust because they train for/in a 'hostile' environment where occlusion and other effects undermine the quality of the data.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-05-28T10:25:18.933Z
Answers.LastActivityDate: 2022-05-28T10:25:18.933Z
Answers.UserDisplayName: emely_pi
Body: <p>Given a dataset of spatial 2D or 3D object map with their bounding box annotations,</p>
  <ol>
  <li>How feasible would it be to train a deep learning model to recognize (classify) &quot;occluded&quot; objects from a 2D or 3D &quot;source&quot; coordinate?</li>
  <li>What model/learning architecture would best work for such a task?</li>
  </ol>
  <p>Example: in this top-view 2D image, two objects are classified &quot;occluded&quot;. Similarly this could be the case in 3D space.</p>
  <p><a href="https://i.sstatic.net/KQOx0.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/KQOx0.png" alt="enter image description here" /></a></p>
  
Tags: <deep-learning><convolutional-neural-networks><object-detection>
Title: Deep Learning for occlusion recognition is 2D or 3D space
UserDisplayName: fhm
ThreadText: # Deep Learning for occlusion recognition is 2D or 3D space
  
  (Asked by: username_0 on 2022-05-28)
  
  Given a dataset of spatial 2D or 3D object map with their bounding box annotations,
  
  1. How feasible would it be to train a deep learning model to recognize (classify) "occluded" objects from a 2D or 3D "source" coordinate?
  2. What model/learning architecture would best work for such a task?
  
  Example: in this top-view 2D image, two objects are classified "occluded". Similarly this could be the case in 3D space.
  
  [![enter image description here](https://i.sstatic.net/KQOx0.png)](https://i.sstatic.net/KQOx0.png)
  
  **Tags:** deep-learning, convolutional-neural-networks, object-detection
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-05-28. Score: 0
  
  What you are looking for are adversial neural networks. GANs or other adversial neural networks are used to classify or detect objects that are partially occluded. They can perform very well given enough training data and tend be quite robust because they train for/in a 'hostile' environment where occlusion and other effects undermine the quality of the data.
  
  
  ---
  
  