@charlesq34 Thanks for sharing the work. @karenachiketc for useful comments/solutions.
There seems to be a lot of people who are confused with regards to data preparation. I noted from the four steps of data preparation.
Mesh sampling using PCL (this is uniform sampling code not the downsampling code) - convert .off to ply, read in pcl and sample it then save as *.txt (similar to PointNet++). Note only reduced point cloud will be left and polygon faces will be lost.
On linux, generate *.so files from pointnet2/tf_ops/sampling/tf_sampling.py. On windows, follow the PointNet++ train file.
Save 2048 points as Hdf5 or just directly train using step 2 for windows.
@charlesq34 @faisal108988
I encountered a problem abuot uniformly sample
I got points cloud of a CAD model from Mesh sampling using PCL . There are some points of CAD's internal structure(Internal points), but pointnet's data do not have internal points.
for example, ModelNet40->airplane_0630
Can you tell me how to remove these internal points? Thank you very much!
@charlesq34 Thanks for sharing the work. @karenachiketc for useful comments/solutions.
There seems to be a lot of people who are confused with regards to data preparation. I noted from the four steps of data preparation.