This code reporsitory implements the framework described in the paper LEGO: Learning Edge with Geometry all at Once by Watching Videos CVPR 2018 (spotlight)
Some more information about this paper: [demo], [presentation], [poster]
If you find this work useful, please consider citing this paper
@inproceedings{yang2018lego,
title={LEGO: Learning Edge with Geometry all at Once by Watching Videos},
author={Yang, Zhenheng and Wang, Peng and Wang, Yang and Xu, Wei and Nevatia, Ram},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={225--234},
year={2018}
}
This code was developed with Tensorflow 1.0, Python 3.4, CUDA 8.0, cuDNN 5.1 and Ubuntu 14.04.
The code takes input data in a certain manner. You can use the scripts in the folder data to be compatible with the data reading. We used two datasets for training in our experiments.
For KITTI, first download the dataset using this script provided on the official website, and then run the following command
python3 data/prepare_train_data.py --dataset_dir=/path/to/raw/kitti/dataset/ --dataset_name='kitti_raw_eigen' --dump_root=/path/to/resulting/formatted/data/ --seq_length=3 --img_width=832 --img_height=256 --num_threads=4For Cityscapes, download the following packages: 1) leftImg8bit_sequence_trainvaltest.zip, 2) camera_trainvaltest.zip. Then run the following command
python3 data/prepare_train_data.py --dataset_dir=/path/to/cityscapes/dataset/ --dataset_name='cityscapes' --dump_root=/path/to/resulting/formatted/data/ --seq_length=3 --img_width=832 --img_height=342 --num_threads=4As the car logo appears in Cityscapes frames, the bottom part is cropped.
Once the data is prepared as described above, the training can be started by run the script:
bash run_train.sh