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

ํ•™์Šต ๋ฐ์ดํ„ฐ ์ƒ์„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

์—๋Ÿฌ ์ฒ˜๋ฆฌ

main.py์˜ #transforms.Lambda(lambda x: logPolar_transform(x)),๋ฅผ ์ฃผ์„ ์ฒ˜๋ฆฌ์‹œ ๋ฌธ์ œ์  ?

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