If change the 'num_views' set in lib/options.py to a value bigger than 1, which stands for multi-view, the network code will have a bug, specificly in the line 114(maybe a little different because I add some comments in my file before) of lib/model/HGPIFuNet.py
pred = in_img[:, None].float() * self.surface_classifier(point_local_feat)
here the size of in_img[:, None].float() is *[Bnum_views, 1, N] and the size of self.surface_classifier(point_local_feat) is [B, 1, N]**. So if num_views=1 as default there won't be this bug, but if set to more than 1, the bug of tensor dimension can't align will occur.
Actually I have just noticed there is a issue before and the author said the code now is optimized for single-view, so that's actually answer my question.
If change the 'num_views' set in lib/options.py to a value bigger than 1, which stands for multi-view, the network code will have a bug, specificly in the line 114(maybe a little different because I add some comments in my file before) of lib/model/HGPIFuNet.py
here the size of in_img[:, None].float() is *[Bnum_views, 1, N] and the size of self.surface_classifier(point_local_feat) is [B, 1, N]**. So if num_views=1 as default there won't be this bug, but if set to more than 1, the bug of tensor dimension can't align will occur.