https://github.com/zhenheny/LEGO

LEGO: Learning Edge with Geometry all at Once by Watching Videos
https://github.com/zhenheny/LEGO

Last synced: about 1 month ago
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LEGO: Learning Edge with Geometry all at Once by Watching Videos

• Host: GitHub
• URL: https://github.com/zhenheny/LEGO
• Owner: zhenheny
• License: mit
• Created: 2018-06-10T20:28:23.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-07-27T04:56:29.000Z (over 6 years ago)
• Last Synced: 2024-08-01T20:36:57.753Z (4 months ago)
• Language: Python
• Size: 17.1 MB
• Stars: 82
• Watchers: 3
• Forks: 18
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# LEGO

This code reporsitory implements the framework described in the paper [*LEGO: Learning Edge with Geometry all at Once by Watching Videos*](https://arxiv.org/abs/1803.05648) CVPR 2018 (**spotlight**)

Some more information about this paper: [[demo](https://www.youtube.com/watch?v=40-GAgdUwI0)], [[presentation](https://youtu.be/WrEKJeK-Wow?t=4628)], [[poster](misc/cvpr18_poster_lego.pdf)]







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](http://www.cvlibs.net/datasets/kitti/raw_data.php), first download the dataset using this [script](http://www.cvlibs.net/download.php?file=raw_data_downloader.zip) provided on the official website, and then run the following command

```bash

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](https://www.cityscapes-dataset.com/), download the following packages: 1) `leftImg8bit_sequence_trainvaltest.zip`, 2) `camera_trainvaltest.zip`. Then run the following command

```bash

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

bash run_train.sh

```