xnowbzhao / sapcu

Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation (CVPR 2022)
MIT License
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A question about "g++ -std=c++11 dense.cpp -O2 -o dense" #20

Open QWTforGithub opened 9 months ago

QWTforGithub commented 9 months ago

Thank you for your code. I have a question about "g++ -std=c++11 dense.cpp -O2 -o dense". I take this command, getting a dense file. Then I take "./dense" to generate "target.xyz", but the file is blank. May I ask you where I made a mistake?

xnowbzhao commented 9 months ago

if you want to use dense alone, you should: 1, rename the point cloud to "test.xyz" and put it to the same folder 2, ./dense 0.004 2048 ←the point number

QWTforGithub commented 9 months ago

if you want to use dense alone, you should: 1, rename the point cloud to "test.xyz" and put it to the same folder 2, ./dense 0.004 2048 ←the point number

Thank you very much for your reply.

  1. Now, I want to use 2048 points to get 8192(4 Ratio), but I take this command "./dense 0.004 2048" to get 498854 points. What should I do?
  2. What should I do to get arbitrary-scale points? Looking forward your reply, thank you!
xnowbzhao commented 9 months ago

./dense is just to obtain the seed points. please follow README for upsampling

QWTforGithub commented 9 months ago

./dense is just to obtain the seed points. please follow README for upsampling

Thank you very much!

QWTforGithub commented 9 months ago

./dense is just to obtain the seed points. please follow README for upsampling

Thank you for your reply. I have another question about "pointcloud_dict['lens']". May I ask you how generating the "pointcloud_dict['lens']"? I found the code using "pointcloud_dict['input'], pointcloud_dict['lens']" during training.

QWTforGithub commented 9 months ago

./dense is just to obtain the seed points. please follow README for upsampling

I want to train your code on other datasets, so I need to know how generating "pointcloud_dict['lens']" (Ground Ttuth), thank you.

xnowbzhao commented 9 months ago

please follow the Dataset