https://github.com/tom-roddick/oft

https://github.com/tom-roddick/oft

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/tom-roddick/oft
• Owner: tom-roddick
• License: mit
• Created: 2019-07-30T15:47:19.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-04-30T04:12:31.000Z (8 months ago)
• Last Synced: 2024-08-01T05:13:22.010Z (4 months ago)
• Language: Python
• Size: 223 KB
• Stars: 198
• Watchers: 14
• Forks: 50
• Open Issues: 15
• Metadata Files:
  • Readme: readme.md
  • License: license.txt

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README

        
# Orthographic Feature Transform for Monocular 3D Object Detection

![OFTNet-Architecture](https://github.com/tom-roddick/oft/raw/master/architecture.png "OFTNet-Architecture")

This is a PyTorch implementation of the OFTNet network from the paper [Orthographic Feature Transform for Monocular 3D Object Detection](https://arxiv.org/abs/1811.08188). The code currently supports training the network from scratch on the KITTI dataset - intermediate results can be visualised using Tensorboard. The current version of the code is intended primarily as a reference, and for now does not support decoding the network outputs into bounding boxes via non-maximum suppression. This will be added in a future update. Note also that there are some slight implementation differences from the original code used in the paper.

## Training

The training script can be run by calling `train.py` with the name of the experiment as a required position argument. 

```

python train.py name-of-experiment --gpu 0

```

By default data will be read from `data/kitti/objects` and model checkpoints will be saved to `experiments`. The model is trained using the KITTI 3D object detection benchmark which can be downloaded from [here](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). See `train.py` for a full list of training options.

## Inference

To decode the network predictions and visualise the resulting bounding boxes, run the `infer.py` script with the path to the model checkpoint you wish to visualise:

```

python infer.py /path/to/checkpoint.pth.gz --gpu 0

```

## Citation

If you find this work useful please cite the paper using the citation below.

```

@article{roddick2018orthographic,  

  title={Orthographic feature transform for monocular 3d object detection},  

  author={Roddick, Thomas and Kendall, Alex and Cipolla, Roberto},  

  journal={British Machine Vision Conference},  

  year={2019}  

}

```