Question on data preprocessing step about im-net-pytorch HOT 4 CLOSED

Hi Supriya,

You were using an old version of the point sampling code. Please use the correct version provided in this repo. Here's the link: https://github.com/czq142857/IM-NET/tree/master/point_sampling

You need to prepare 256^3 voxels to use that code. You also need to rewrite a few lines to read .binvox files instead of .mat files.

The old point-sampling code can be used if you wish so. You just need to make sure the points are scaled correctly. Specifically, rewrite this line in modelAE.py according to the sampling resolution (note the number 256):

			self.data_points = (data_dict['points_'+str(self.sample_vox_size)][:].astype(np.float32)+0.5)/256-0.5

Best,
Zhiqin

from im-net-pytorch.

Thank you so much for the help @czq142857 !

I am using 2_gather_256vox_16_32_64.py to sample point-value pairs. I noticed that, you are transforming coordinates from Shapenet.v1 to Shapenet.v2 on line 104. What is the purpose of this conversion? The ready-to-use dataset is based on ShapeNetCore.v1 or ShapeNetCore.v2 dataset?

Also, just want to confirm, I don't need to use flood-filling code to make the mesh watertight, as 2_gather_256vox_16_32_64.py uses voxel carving for this purpose. Is that right?

Thank you for your time.

from im-net-pytorch.

The conversion does not have a lot of meaning. You can use either Shapenet v1 or v2 coordinates based on your preferences.

The ready-to-use dataset is based on ShapeNetCore.v1, but in ShapeNetCore.v2 coordinates.

Right. You do not need to use flood-filling code to make the mesh watertight, as 2_gather_256vox_16_32_64.py uses voxel carving for this purpose.

from im-net-pytorch.

Thank you for the help!

from im-net-pytorch.