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Hi,
h_i_s and h_j_t mean the deep features of source and target samples respectively and i and j represent the i-th and j-th index of the sample. We directly extract the h from the pointnet.
Thanks for your reply! It seems that h_i_s and h_i_t just mean SA node features, and they are obtained by concatenating all node features together since it can be seen from in line #124 or line #l29 of Model.py:feat_node = feat_ori.view(batch_size, -1). Is that correct?
Hi, I am confused about the formula(6)in the paper. What does h_i_s/h_i_t in the formula(6) mean and how is it calculated?
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