https://github.com/alitabet/morton-net

PyTorch implementation to train MortonNet and use it to compute point features. MortonNet is trained in a self-supervised fashion, and the features can be used for general tasks like part or semantic segmentation of point clouds.
https://github.com/alitabet/morton-net

computer-vision deep-learning mortonnet point-cloud pytorch s3dis segmentation self-supervised-learning shapenet vkitti

Last synced: about 2 months ago
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PyTorch implementation to train MortonNet and use it to compute point features. MortonNet is trained in a self-supervised fashion, and the features can be used for general tasks like part or semantic segmentation of point clouds.

• Host: GitHub
• URL: https://github.com/alitabet/morton-net
• Owner: alitabet
• Created: 2019-05-22T08:10:59.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-05-29T13:01:37.000Z (over 5 years ago)
• Last Synced: 2024-07-21T21:43:34.456Z (2 months ago)
• Topics: computer-vision, deep-learning, mortonnet, point-cloud, pytorch, s3dis, segmentation, self-supervised-learning, shapenet, vkitti
• Language: Python
• Homepage: https://www.alithabet.com/mortonnet
• Size: 454 KB
• Stars: 20
• Watchers: 5
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds

![Morton order](doc/pull.png)

MortonNet is a self-supervised approach for point cloud processing. 

MortonNet learns local geometric features for each point. Given a

point cloud, our approach creates sequences for each point. These

sequences are based on the popular Morton order. With a sequence of

`N` points, MortonNet uses the first `N - 1` points and learns to

predict the last point. We show how to use MortonNet with the

[S3DIS Dataset](http://buildingparser.stanford.edu/dataset.html).

### Data

We download and prepare the data using the tools provided by 

[Engelmann et. al.](https://www.vision.rwth-aachen.de/media/papers/PID4967025.pdf)

available [here](https://github.com/VisualComputingInstitute/3d-semantic-segmentation).

To download the dataset fill in this [form](https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1).

Next we process the data and convert to npy files for processing. This code is from

[Engelmann et. al.](https://github.com/VisualComputingInstitute/3d-semantic-segmentation):

```commandline

python prepare_s3dis.py --input_dir path/to/dataset --output_dir path/to/output

```

### Installation

All the required packages are in `environment.yml`. If you use conda,

you can directly run:

```commandline

conda env create -f environment.yml

```

If you prefer to use `pip` or any other alternative, you can create your

own requirements file using the packages and versions provided. This code 

was tested with Python 3.6 in Ubuntu 16.04, running CUDA 9.0 and cuDNN 7.4.1. All the package versions are 

available in the environment file. 

### Computing Sequences

Before training MortonNet, you have to compute the sequences to

feed in the self-supervised training scheme. To create sequences,

run the following:

```commandline

python tools/create_sequences.py --root_dir path/to/dataset --out_dir path/to/save.dir --phase train/valid/test

```

Note that the `path/to/dataset` here corresponds to the path with the npy files. 

By default this will compute sequences for all the available files. You

can specify a file id using the argument `--data_idx`. For more information

about available commands run the `--help` or `-h` options.

### Training MortonNet

Once the sequences are precomputed, you can train MortonNot with:

```commandline

python train.py --root_dir path/to/sequences --model_dir path/to/checkpoint/save

```

There are different ways to configure MortonNet parameters. Possible 

configurations are in `tools/config.py`. If you create your own config,

you can load it using argument `--config_type`. You can also specify 

other parameters like using multi GPUs, batch size, and others. For a

full list run the `--help` or `-h` options.

### Computing features

You can use the trained MortonNet to compute features for any point

cloud dataset. The features of every sequence come from the last hidden

state of the RNN. Since every point gets 5 sequences during sequence

computation, we can merge these features in different ways. We output

all 5 features for each point, which can later be concatenated or pooled.

To compute features run:

```commandline

python compute_features.py --root_dir path/to/sequences --best_path path/to/mortonnet/model --out_dir path/to/save/features

```

### Citation

If you find our work helpful, please cite our paper.

```

@misc{thabet2019mortonnet,

    title={MortonNet: Self-Supervised Learning of Local Features in 3D Point Clouds},

    author={Ali Thabet and Humam Alwassel and Bernard Ghanem},

    year={2019},

    eprint={1904.00230},

    archivePrefix={arXiv},

    primaryClass={cs.CV}

}

```