Answers.Body: <p>The previous milestone of your question is <a href="https://ai.stackexchange.com/questions/39478/each-neuron-looks-one-area-or-the-whole-image">this*</a></p>
  <p>You can use data stack that it has low resolution as <i>S(Y<sub>n</sub>)</i> of stream from supervised network for verifying. No data transfer loss with this rule:</p>
  <p><i>S(X<sub>n</sub>) ≥ S(Y<sub>n</sub>) &gt; S(X<sub>n</sub>)/2</i></p>
  <p>Each time you change tether provider that network model before transform data stacks for the default stream or more data stack may be required because less finite sub-states in the supervised network will not be a factor in diagnosis.</p>
  <p>There is an invert ratio between signal complexivity of <i>X</i> and process accuracy in <i>S(Y<sub>n</sub>) ≤ S(X<sub>n</sub>)/2</i> rule.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-03-09T16:41:59.910Z
Answers.Text: As it’s currently written, your answer is unclear. Please [edit] to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers [in the help center](/help/how-to-answer).
Answers.UserDisplayName: Community
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-03-16T11:57:08.443Z
Answers.Text: Unfortunately I do not understand your answer either. Your linked post is also inaccessible..
Answers.UserDisplayName: nmb
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-03-16T16:14:48.520Z
Answers.Text: Hmm... Link is broken. It fixed.
  If can help you this, <i>Y<sub>n</sub></i> is your check data in one process time. Obtain check data by new tether before decode this by referencing first tether in every process time. <i>Y</i> is your check data stack and syntesis doors by this. This dynamic to be more successful than using static tethering methods because doors have exceptions of finite sub processes that they can not detected approximately.
Answers.UserDisplayName: fkybrd
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-04-17T12:31:25.033Z
Answers.Text: You can use mathjax on this site!
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-04-19T08:59:14.997Z
Answers.Text: Rules are not providing my requirements on this site. You can delete those
Answers.UserDisplayName: fkybrd
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-03-09T12:11:56.367Z
Answers.LastActivityDate: 2023-03-16T16:16:20.020Z
Answers.LastEditDate: 2023-03-16T16:16:20.020Z
Answers.UserDisplayName: fkybrd
Body: <p>I have a huge dataset of 3D point clouds (each point consists of X,Y,Z coordinates) and another dataset with keypoints (also X,Y,Z) which lie on quite recognizable structures in the point cloud. As a human it is pretty easy to find these keypoint given the according point cloud.</p>
  <p>There are 6-24 keypoints for each point cloud which may have up to 100.000 points but can be downsampled to fit into memory while processing.</p>
  <p>My goal is to train a supervised deep learning model to detect/generate these keypoints in new point clouds. These have to be new synthetic points since the keypoints are most likely not in my point cloud.</p>
  <p>Previously i tried to train a regression model using PointNet++ semantic segmentation. I generated weights for each point in the input point cloud according to its proximity to the next keypoint. For new point clouds the weights were predicted pretty well but it is very difficult to regenerate the synthetic keypoints from these weights since the area around the keypoint can be very sparse.</p>
  <p>Essentially i am trying to use a model like this <a href="https://github.com/caiyuanhao1998/RSN" rel="nofollow noreferrer">RSN</a> on 3D point clouds to detect synthetic keypoints in the data. I thought about using object detection models like <a href="https://github.com/steph1793/Voxelnet" rel="nofollow noreferrer">VoxelNet</a> but these detect bounding boxes and i want to get a single predicted point as a result.</p>
  <p>I am also wondering if its worth looking into GANs but i dont have experience with these models and until now i only found models who generate a whole new point cloud instead of a single points inside a given point cloud.</p>
  <p>I would love to hear some ideas from you to point me in the right direction.</p>
  
Tags: <deep-learning><generative-adversarial-networks><object-detection><generative-model><geometric-deep-learning>
Title: Keypoint generation in 3D point clouds with Deep Learning
UserDisplayName: nmb
ThreadText: # Keypoint generation in 3D point clouds with Deep Learning
  
  (Asked by: username_0 on 2023-03-03)
  
  I have a huge dataset of 3D point clouds (each point consists of X,Y,Z coordinates) and another dataset with keypoints (also X,Y,Z) which lie on quite recognizable structures in the point cloud. As a human it is pretty easy to find these keypoint given the according point cloud.
  
  There are 6-24 keypoints for each point cloud which may have up to 100.000 points but can be downsampled to fit into memory while processing.
  
  My goal is to train a supervised deep learning model to detect/generate these keypoints in new point clouds. These have to be new synthetic points since the keypoints are most likely not in my point cloud.
  
  Previously i tried to train a regression model using PointNet++ semantic segmentation. I generated weights for each point in the input point cloud according to its proximity to the next keypoint. For new point clouds the weights were predicted pretty well but it is very difficult to regenerate the synthetic keypoints from these weights since the area around the keypoint can be very sparse.
  
  Essentially i am trying to use a model like this [RSN](https://github.com/caiyuanhao1998/RSN) on 3D point clouds to detect synthetic keypoints in the data. I thought about using object detection models like [VoxelNet](https://github.com/steph1793/Voxelnet) but these detect bounding boxes and i want to get a single predicted point as a result.
  
  I am also wondering if its worth looking into GANs but i dont have experience with these models and until now i only found models who generate a whole new point cloud instead of a single points inside a given point cloud.
  
  I would love to hear some ideas from you to point me in the right direction.
  
  **Tags:** deep-learning, generative-adversarial-networks, object-detection, generative-model, geometric-deep-learning
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-03-09. Score: 0
  
  The previous milestone of your question is [this\*](https://ai.stackexchange.com/questions/39478/each-neuron-looks-one-area-or-the-whole-image)
  
  You can use data stack that it has low resolution as *S(Yn)* of stream from supervised network for verifying. No data transfer loss with this rule:
  
  *S(Xn) ≥ S(Yn) > S(Xn)/2*
  
  Each time you change tether provider that network model before transform data stacks for the default stream or more data stack may be required because less finite sub-states in the supervised network will not be a factor in diagnosis.
  
  There is an invert ratio between signal complexivity of *X* and process accuracy in *S(Yn) ≤ S(Xn)/2* rule.
  
  > **Comments:**
  > **username_2**: As it’s currently written, your answer is unclear. Please [edit] to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  > **username_0**: Unfortunately I do not understand your answer either. Your linked post is also inaccessible..  
  > **username_1**: Hmm... Link is broken. It fixed. If can help you this, <i>Y<sub>n</sub></i> is your check data in one process time. Obtain check data by new tether before decode this by referencing first tether in every process time. <i>Y</i> is your check data stack and syntesis doors by this. This dynamic to be more successful than using static tethering methods because doors have exceptions of finite sub processes that they can not detected approximately.  
  > **username_3**: You can use mathjax on this site!  
  > **username_1**: Rules are not providing my requirements on this site. You can delete those  
  
  ---
  
  