Hello Thomas,
Thanks for the reply.

A blunt way to mimic what a histogram does is to quantize the coordinates into integers starting from 0 and then use them as the indices of a sparse tensor. These are allowed to have duplicate indices. Then tensor.coalesce() will sum all values at a given coordinate.

Could you please elaborate on the quantizing part?  If suppose my batched point cloud A = torch.randn((32, 10000, 3)) is quantized to a variable B (an integer) such that B.shape == (32, 10000, 3) with each value pointing to a place where corresponding point in A falls into the grid of shape (64,64,64) that would be excellent for my usecase.
With those indices, I can then do scatter_mean over my feature tensor.
Thank you