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Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation

★ 68 Open GitHub ↗

Self-supervised method for scene-flow estimation of LiDAR point clouds. Method is trained and tested on the nuScenes and KITTI datasets in TensorFlow. (CVPR 2020)

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# Just Go with the Flow: Self-Supervised Scene Flow Estimation, CVPR 2020 (Oral)

Authors: [Himangi Mittal](https://github.com/HimangiM), [Brian Okorn](https://github.com/bokorn), [David Held](https://github.com/davheld)

[[arxiv](https://arxiv.org/pdf/1912.00497.pdf)] [[Project Page](https://just-go-with-the-flow.github.io/)]

### Citation
If you find our work useful in your research, please cite:
```
@InProceedings{Mittal_2020_CVPR,
author = {Mittal, Himangi and Okorn, Brian and Held, David},
title = {Just Go With the Flow: Self-Supervised Scene Flow Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
```

### Introduction
In this work, we propose a method of scene flow estimation using two self-supervised losses, based on nearest neighbors and cycle consistency. These self-supervised losses allow us to train our method on large unlabeled autonomous driving datasets; the resulting method matches current state-of-the-art supervised performance using no real world annotations and exceeds stateof-the-art performance when combining our self-supervised approach with supervised learning on a smaller labeled dataset.

For more details, please refer to our [paper](https://arxiv.org/pdf/1912.00497.pdf) or [project page](https://just-go-with-the-flow.github.io/).

### Installation 
#### Requirements
   ```
   CUDA 9.0  
   Tensorflow-gpu 1.9
   Python 3.5
   g++ 5.4.0
   ```
#### Steps
  (a). Clone the repository.
  ```
  git clone https://github.com/HimangiM/Self-Supervised-Scene-Flow-Estimation.git
  ```
  (b). Install dependencies
  ```
  Create a virtualenv
  python3 -m venv sceneflowvenv
  source sceneflowvenv/bin/activate
  cd Self-Supervised-Scene-Flow-Estimation
  pip install -r requirements.txt
  ```
  ```
  Check for CUDA-9.0
  ```
  (c). Compile the operations
  The ```TF operators``` are included under src/tf_ops. Check the [CUDA compatability](https://en.wikipedia.org/wiki/CUDA#GPUs_supported) and ```edit the architecture``` accordingly in ```makefiles``` of each folder (tf_ops/sampling, tf_ops/grouping, tf_ops/3d_interpolation) The authors had used ```sm_61``` as the architecture for CUDA-9.0. Finally, move into each directory and run ```make```. Also, check for the path for CUDA-9.0 and edit the path in the makefiles of each folder. If this method throws error, then run ```bash make_tf_ops.sh sm_61```.
    
### Datasets
   Download the kitti dataset from the Google Drive [link](https://drive.google.com/drive/u/1/folders/1WNqrfUBR-EdN2ns_0D3FIdJBAPmFkaOo). Each file is in the .npz format and has three keys: ```pos1, pos2 and gt```, representing the first frame of point cloud, second frame of point cloud and the ground truth scene flow vectors for the points in the first frame. Create a folder with name data_preprocessing and download the kitti dataset in it. The dataset directory should look as follows:
   ```
   Self-Supervised-Scene-Flow-Estimation
   |--data_preprocessing
   |  |--kitti_self_supervised_flow
   |  |  |--train
   |  |  |--test
   ```
   The data preprocessing file to run the code on KITTI is present in the src folder: kitti_dataset_self_supervised_cycle.py. 
   To create a dataloader for own dataset, refer to the script:
   ```
   nuscenes_dataset_self_supervised_cycle.py
   ```
  
### Training and Evaluation
   To train on own dataset, refer to the scripts:
   ```
   train_1nn_cycle_nuscenes.py
   bash src/commands/command_train_cycle_nuscenes.sh
   ```
   To evaluate on the KITTI dataset, execute the shell script:
   ```
   bash src/commands/command_evaluate_kitti.sh
   ```
   [Link](https://drive.google.com/drive/folders/1ldakyGw4QxfhAQeykyoJG2iv8yNuAy5_?usp=sharing) to the pretrained model.
  
### Visualization
You can use ```Open3d``` to visualize the results. A sample script is given in visualization.py

Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation

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