fxia22/pointnet.pytorch

Different number of points

Amir-Abi opened this issue · 3 comments

Hi, point net was originally trained on a fixed-sized input , and I wonder if there is a efficient way to deal with different numbers of points in a batch. for example, (3, 10) and (3, 11) are my samples, how I should use them in the same batch ??? is there any better way than padding & passing mask that it'd used in NLP ???

I got same question here. But it seems pytorch requires same data shape when stacking into batches

I got same question, how you solve the problem?

I think you should implement a collate function and pass it to the DataLoader so you can get heterogonous batches