DenseLidarNet (50%)
Generating Dense Lidar Data using cues from monocular image and sparse lidar data.

์ค์น
python 2.7
pip install torch==0.4.1 -f https://download.pytorch.org/whl/cu80/stable
pip install https://download.pytorch.org/whl/torchvision-0.1.6-py2-none-any.whl
pip install tqdm h5py ipdb
ํ์ต ๋ฐ์ดํฐ ์์ฑ (KITTI)
$ vi utils/datagen_v2.py
self.kitti_img_dir = '/media/adioshun/data/datasets/training/image_2/'
self.kitti_calib_dir = '/media/adioshun/data/datasets/training/calib/'
self.kitti_label_dir = '/media/adioshun/data/datasets/training/label_2/'
self.kitti_lidar_dir = '/media/adioshun/data/datasets/training/velodyne'
self.dump_dir = '../../data/'
์คํ
$ python code/scripts/init_state_dict.py -> init_state_dict.py
$ python train.py -tp1 /tmp/DenseLidarNet/lidar_pts -tp2 /tmp/DenseLidarNet/tf_lidar_pts -tp3 /tmp/DenseLidarNet/bbox_info -vp1 /tmp/DenseLidarNet/lidar_pts -vp2 /tmp/DenseLidarNet/tf_lidar_pts -vp3 /tmp/DenseLidarNet/bbox_info
$ python train.py -e
์๋ฌ ์ฒ๋ฆฌ
train.py
์๋ฌ ์์ ์ฝ๋Part03-Chapter02-DenseLidarNet_train.py
main.py์ #transforms.Lambda(lambda x: logPolar_transform(x)),
๋ฅผ ์ฃผ์ ์ฒ๋ฆฌ์ ๋ฌธ์ ์ ?
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