Hi, Julian
How are you doing? thanks for sharing your excellent work to public.
I have a question about voxelization
Does voxelize.py and voxelized_pointcloud_sampling.py do exactly the same thing but with different code ?
I see you use voxelize.py for super_resolution work and use voxelized_pointcloud_sampling.py for Point Cloud Completion ?
for generating input occupancy volume, is there any difference between the two?
Is that occupancy volume for voxelize.py directly obtained from mesh , therefore only if voxel cross any triangle surface, that voxel is occupied --(input occupancy volume is always dense)?
and voxelized_pointcloud_sampling.py do sampling a predefined number of points on mesh first, then the voxelized occupancy volume somehow depends on both number of points and voxel resolution ? So where would be some empty voxels even that voxel is supposed to be on surface (input occupancy volume is sparse maybe)?
Yet another question, for Voxel Super-Resolution work, I see from paper that voxel with 128 get better reconstruction, that is what you want to tell in paper that higher voxel resolution can give better reconstruction (super resolution) ?
For the three tasks of voxel super-resolution , point cloud completion and SVR, all the networks use input occupancy volume (but different resolution ) and output occupancy , right ?
Hi, Julian How are you doing? thanks for sharing your excellent work to public.
I have a question about voxelization Does voxelize.py and voxelized_pointcloud_sampling.py do exactly the same thing but with different code ? I see you use voxelize.py for super_resolution work and use voxelized_pointcloud_sampling.py for Point Cloud Completion ? for generating input occupancy volume, is there any difference between the two?
Is that occupancy volume for voxelize.py directly obtained from mesh , therefore only if voxel cross any triangle surface, that voxel is occupied --(input occupancy volume is always dense)?
and voxelized_pointcloud_sampling.py do sampling a predefined number of points on mesh first, then the voxelized occupancy volume somehow depends on both number of points and voxel resolution ? So where would be some empty voxels even that voxel is supposed to be on surface (input occupancy volume is sparse maybe)?
Yet another question, for Voxel Super-Resolution work, I see from paper that voxel with 128 get better reconstruction, that is what you want to tell in paper that higher voxel resolution can give better reconstruction (super resolution) ?
For the three tasks of voxel super-resolution , point cloud completion and SVR, all the networks use input occupancy volume (but different resolution ) and output occupancy , right ?
Thanks a lot for your time.
Best regards