3D UNet with TorchSparse

[victoryc]

By chance, has anyone implemented 3D UNet with TorchSparse? If so, I would appreciate a link.

The specific issue I am currently pondering about is how to concatenate the features at a level with the up-sampled features from lower level. I looked to see whether there is a built-in function to do that. The functions I saw didn't seem to be great fits.

Things I saw:

• The add function in SparseTensor: This assumes that the coordinates in the two tensors are identical, i.e., the ".C" field is the same in the two tensors. But, this need not be the situation in the UNet case.
• sparse_collate_tensors function in helpers.py : Seems to be for the purpose of creating a tensor where in multiple tensors are added into a batch (?).

Thanks.

[victoryc]

Oops, posted in the wrong place. Closing this here and posting in TorchSparse board.