Closed aldinorizaldy closed 9 months ago
Hi @aldinorizaldy,
Because S3DIS dataset is small enough to be loaded entirely in memory, we preload the point clouds and their colors in this function:
More precisely here: https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/8ec7478cd6b5fa3c6f423b984d6817b5e90e057b/datasets/S3DIS.py#L757-L790
If you can do the same then great. If not you might have to load from file directly in the get_item function. Actually if your dataset consists of several small file (lets say various different rooms) It might be better to load it directly in the get_item function to avoid using to much memory by preloading everything
Hi Thomas,
Perfect!! Thank you for answering my question. I had an error with the expected_N
value, but I solved it by reducing the value into 1e4. Now I'm able to train my custom data.
It is an outdoor lidar with sparse point density. So I have to play around with the in_radius
and first_subsampling_dl
. The average point spacing is 2 m, so I chose first_subsampling_dl
= 2 and in_radius
= 100, following your advice to have a consant ratio more or less 50x. Having less values for both parameters gave me worse results.
Thank you again for still being here and help the community. I do really appreciate it. Cheers!!
Thanks for your kind message, and good luck with your problem!
Best, Hugues
Hi Thomas, thanks for the great works. I hope you are still around to answer my questions.
I am working on a point cloud data with hyperspectral features, so you can think about point with XYZ + RGB + 50 features. I know how you prepared S3DIS dataset by having 5 features (1 + RGB + height) for the
in_features_dim
. But how if I have more features? I would think of 1 + RGB + 1 height + 50 = 55. Is that correct?If so, where should I change the code? I see there are several things to do:
in_features_dim
to 55 https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/8ec7478cd6b5fa3c6f423b984d6817b5e90e057b/train_S3DIS.py#L146elif self.config.in_features_dim == 55:
https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/8ec7478cd6b5fa3c6f423b984d6817b5e90e057b/datasets/S3DIS.py#L418-L419input_colors
collect the RGB features? If I have RGB + 50 more features, then how should I change this code? https://github.com/HuguesTHOMAS/KPConv-PyTorch/blob/8ec7478cd6b5fa3c6f423b984d6817b5e90e057b/datasets/S3DIS.py#L353-L372Thanks!!