Download all point clouds and labels from Semantic3D Dataset and place extracted training files to $SEMA3D_DIR/data/train, reduced test files into $SEMA3D_DIR/data/test_reduced, and full test files into $SEMA3D_DIR/data/test_full, where $SEMA3D_DIR is set to dataset directory. The label files of the training files must be put in the same directory than the .txt files.
To compute the partition with handcrafted features run:
python partition/partition.py --dataset sema3d --ROOT_PATH $SEMA3D_DIR --voxel_width 0.05 --reg_strength 0.8 --ver_batch 5000000
It is recommended that you have at least 24GB of RAM to run this code. Otherwise, increase the voxel_width parameter to increase pruning.
Then, reorganize point clouds into superpoints by:
python learning/sema3d_dataset.py --SEMA3D_PATH $SEMA3D_DIR
To train on the whole publicly available data and test on the reduced test set, run:
CUDA_VISIBLE_DEVICES=0 python learning/main.py --dataset sema3d --SEMA3D_PATH $SEMA3D_DIR --db_test_name testred --db_train_name trainval \
--epochs 500 --lr_steps '[350, 400, 450]' --test_nth_epoch 100 --model_config 'gru_10,f_8' --ptn_nfeat_stn 11 \
--nworkers 2 --pc_attrib xyzrgbelpsv --odir "results/sema3d/trainval_best"
The trained network can be downloaded here and loaded with --resume argument. Rename the file model.pth.tar (do not try to unzip it!) and place it in the directory results/sema3d/trainval_best.
To test this network on the full test set, run:
CUDA_VISIBLE_DEVICES=0 python learning/main.py --dataset sema3d --SEMA3D_PATH $SEMA3D_DIR --db_test_name testfull --db_train_name trainval \
--epochs -1 --lr_steps '[350, 400, 450]' --test_nth_epoch 100 --model_config 'gru_10,f_8' --ptn_nfeat_stn 11 \
--nworkers 2 --pc_attrib xyzrgbelpsv --odir "results/sema3d/trainval_best" --resume RESUME
We validated our configuration on a custom split of 11 and 4 clouds. The network is trained as such:
CUDA_VISIBLE_DEVICES=0 python learning/main.py --dataset sema3d --SEMA3D_PATH $SEMA3D_DIR --epochs 450 --lr_steps '[350, 400]' --test_nth_epoch 100 \
--model_config 'gru_10,f_8' --pc_attrib xyzrgbelpsv --ptn_nfeat_stn 11 --nworkers 2 --odir "results/sema3d/best"
Not yet available.
To upsample the prediction to the unpruned data and write the .labels files for the reduced test set, run (quite slow):
python partition/write_Semantic3d.py --SEMA3D_PATH $SEMA3D_DIR --odir "results/sema3d/trainval_best" --db_test_name testred
To visualize the results and intermediary steps (on the subsampled graph), use the visualize function in partition. For example:
python partition/visualize.py --dataset sema3d --ROOT_PATH $SEMA3D_DIR --res_file 'results/sema3d/trainval_best/prediction_testred' --file_path 'test_reduced/MarketplaceFeldkirch_Station4' --output_type ifprs
avoid --upsample 1 as it can can take a very long time on the largest clouds.