Hi, I am slightly confused about a couple of things.
Why are there two separate dataloaders for train_semseg and test_semseg in S3DISDataLoader.py? Both seem to use different techniques for subsampling of points.
I am confused regarding the concept of hierarchical partitioning of input point clouds, there seems to be a sampling done in the dataloaders as well as the PointNetAbstraction layers. However, The PointNet++ paper only mentions furthest point sampling and a volumetric ball query around it. Why is that?
The S3DISDataset seems to use a random sampling technique to get blocks of fixed number of points whereas ScannetDatasetWholeScene uses grid sampling to get a block of fixed number of points. Why is this done? Would this not give inaccurate results?
Hi, I am slightly confused about a couple of things.