Skip to content
LEGO: Learning Edge with Geometry all at Once by Watching Videos
Branch: master
Clone or download
Latest commit 39cd11a Jul 27, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data add mostly files, need to add documentation Jul 2, 2018
depth2normal add mostly files, need to add documentation Jul 2, 2018
eval add mostly files, need to add documentation Jul 2, 2018
misc Add files via upload Jul 2, 2018
LEGOLearner.py add mostly files, need to add documentation Jul 2, 2018
LICENSE Create LICENSE Jul 27, 2018
README.md Update README.md Jul 2, 2018
main.py
nets.py add mostly files, need to add documentation Jul 2, 2018
run_train.sh add mostly files, need to add documentation Jul 2, 2018
train.py add mostly files, need to add documentation Jul 2, 2018
utils.py add mostly files, need to add documentation Jul 2, 2018

README.md

LEGO

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}
}

Prerequisites

This code was developed with Tensorflow 1.0, Python 3.4, CUDA 8.0, cuDNN 5.1 and Ubuntu 14.04.

Preparing training data

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=4

For 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=4

As the car logo appears in Cityscapes frames, the bottom part is cropped.

Training

Once the data is prepared as described above, the training can be started by run the script:

bash run_train.sh
You can’t perform that action at this time.