About Scannet preparation

Hi, thanks for your excellent work!

I got the Scannet dataset download code. But I am not sure which type of data format is required for silk evalution and training?

Could you give me any suggestion about how to download Scannet and prepare the dataset in a correct way?

The config of scannet download are as follows:

parser = argparse.ArgumentParser(description='Downloads ScanNet public data release.')
parser.add_argument('-o', '--out_dir', default='/data0/', help='directory in which to download')
parser.add_argument('--task_data', action='store_true', help='download task data (v1)')
parser.add_argument('--label_map', action='store_true', help='download label map file')
parser.add_argument('--v1', action='store_true', help='download ScanNet v1 instead of v2')
parser.add_argument('--id', help='specific scan id to download')
parser.add_argument('--preprocessed_frames', action='store_true', help='download preprocessed subset of ScanNet frames (' + PREPROCESSED_FRAMES_FILE[1] + ')')
parser.add_argument('--test_frames_2d', action='store_true', help='download 2D test frames (' + TEST_FRAMES_FILE[1] + '; also included with whole dataset download)')
parser.add_argument('--type', help='specific file type to download (.aggregation.json, .sens, .txt, _vh_clean.ply, _vh_clean_2.0.010000.segs.json, _vh_clean_2.ply, _vh_clean.segs.json, _vh_clean.aggregation.json, _vh_clean_2.labels.ply, _2d-instance.zip, _2d-instance-filt.zip, _2d-label.zip, _2d-label-filt.zip)')
args = parser.parse_args()

How can I make a mini effort to setup the scannet dataset for silk repo?

Thanks for your time!
Haolin

The issue names only the ScanNet download script and its argparse options, not a repository file or test. Start by locating the SiLK training and evaluation entry points that consume ScanNet data, then document the required file types, preparation steps, and a minimal download configuration. Done means a newcomer can set up the dataset and know which command or workflow verifies it.

Written by the indexing model from the issue text.