julessanchez / coarselabel

Helper functions and illustrations of coarse label for semantic segmentation

CoarseLabel

Helper functions and illustrations of COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets.

Each dataset folder present the following configuration:

• DatasetName
  • map_DatasetName_to_CL.py (can be launched to read (and optionnaly save) a frame for the target dataset with the coarse labels)
  • table_DatasetName_to_CL.py (map the original label of the target dataset to the coarse labels)
  • relabel_DatasetName_to_CL.py (relabel a full sequence of a dataset)

An example to save Coarse Labels from the frame 10 of the sequence 03 SemanticKITTI:

python SemanticKITTI/map_SemanticKITTI_to_CL.py -f 10 -s 3 -d semanticKITTIDirectory --saved 1

An other example to process the full sequence 03 of SemanticKITTI:

python SemanticKITTI/relabel_SemanticKITTI_to_CL.py -s 3 -d semanticKITTIDirectory

Link for the SRU-Net and SPVCNN pretrained model (with SemanticPOSS as the target): https://cloud.mines-paristech.fr/index.php/s/555t3DzzMmhA2xp

SRU-Net is the default model from : https://github.com/facebookresearch/PointContrast

SPVCNN is the default model from : https://github.com/mit-han-lab/spvnas

coarselabel's Issues

Question about your approach vs modifying learning map?

Hi and thanks for your work!

What's the advantage of using your script instead of modifying the learning_map entry in the semantic kitti config file?