Multi-Echo Denoising in Adverse Weather
Environment:
- Python 3.8.10
- CUDA 11.6
- PyTorch 1.12.1+cu116
- Numpy 1.23.3
Datasets:
Collect corrupted point clouds from STF:
cd utils
python3 create_new_stf.py -d root/STF_dataset/ -n root_for_new_dataset/
Train:
cd networks
./self_train.sh -d root/snowyKITTI/dataset/ -a smednet.yml -l /your/log/folder/ -c 0
./multi_self_train.sh -d root/new_STF/dataset/ -a smednet.yml -l /your/log/folder/ -c 0
Infer (pretrained singe-echo model -m root/logs/2023-2-21-15:49/, multi-echo model -m root/logs/2023-2-27-13:11/):
cd networks/train/tasks/semantic
python3 self_infer.py -d root/snowyKITTI/dataset/ -m root/logs/2023-2-21-15:49/ -l /your/predictions/folder/ -s test
python3 multi_self_infer.py -d root/new_STF/dataset/ -m root/logs/2023-2-27-13:11/ -l /your/predictions/folder/ -s test
(-s = split)
Evaluate:
cd networks/train/tasks/semantic
python3 evaluate_iou.py -d root/snowyKITTI/dataset/ -dc root/networks/train/tasks/semantic/config/labels/snowy-kitti.yaml -p /your/predictions/folder/ -s test
(-s = split)
Visualize:
cd utils
single-echo:
python3 visualize.py -d root/snowyKITTI/dataset/ -c root/networks/train/tasks/semantic/config/labels/snowy-kitti.yaml -p /your/predictions/folder/ -s 22
multi-echo:
python3 visualize.py -d root/new_STF/dataset/ -c root/networks/train/tasks/semantic/config/labels/stf.yaml -p /your/predictions/folder/ -s 4 -me
(-me = multi-echo)