https://github.com/Kshitizbansal/pointillism-multi-radar-data

Multiple Radar Dataset release for Pointillism paper.
https://github.com/Kshitizbansal/pointillism-multi-radar-data

Last synced: 6 days ago
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Multiple Radar Dataset release for Pointillism paper.

• Host: GitHub
• URL: https://github.com/Kshitizbansal/pointillism-multi-radar-data
• Owner: Kshitizbansal
• License: cc0-1.0
• Created: 2021-09-14T19:52:20.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-08-26T00:26:59.000Z (about 2 years ago)
• Last Synced: 2024-08-02T21:38:37.352Z (3 months ago)
• Language: Python
• Size: 229 KB
• Stars: 12
• Watchers: 2
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Pointillism: A Multi-modal dataset for automotive radar sensing.

Official multi-radar Dataset release for [Pointillism: Accurate 3D Bounding Box Estimation with Multi-Radars](https://wcsng.ucsd.edu/pointillism/). Official code for the paper is available at [RP-net](https://github.com/Kshitizbansal/pointillism_rp_net).

## Sensor setup

![pointillism sensor setup](pointillism_setup.png)

* 1 x 16-channel OS1 Ouster LiDAR

* 1 x RealSense D415 Camera (for RGB only)

* 2 x IWR1443BOOST 3T4R Radars

## Dataset

About `300` frames are provided from each sensor for `48` different scenes. Each frame is time synchronized among all the sensors using system timestamps.

## Data Visualizer SDK

### Requirements for Visualizer

1) Download the dataset from https://drive.google.com/file/d/1C-Ryh5W5FLPenNgPUDNcNwiac-NnwU1A/view?usp=sharing in data folder.

2) Dataset in **pointillism-multi-radar-data** should follow this directory structure. 

    ```

    pointillism-multi-radar-data

    └───data

        └───scene{#}       'Where {} is omitted and # is the folder number'

            └───lidar

                └───*.pcd

            └───radar_0

                └───*.csv

            └───radar_1

                └───*.csv

            └───images

                └───*.jpg

            └───label

                └───*.json

    ```       

3) Download **ffmpeg** https://ffmpeg.org/download.html and add ffmpeg.exe to path environment variable (only required to create video).

4) Download **open3d**, http://www.open3d.org/download/, for 3D visualization.

  

### To run Visualizer:

```

To see all options type 

  python visualization.py -h

usage: visualization.py [-h] 

                        --type  [{Lidar3D,LidarBird,Camera,Radar3D,RadarBird}]

                        [--radar [RADAR [RADAR ...]]] 

                        [--video] 

                        --frame [FRAME [FRAME ...]] 

                        --dataset [DATASET [DATASET ...]]

Enter which file and type of image to be converted

optional arguments:

  -h, --help            show this help message and exit

  --type [{Lidar3D,LidarBird,Camera,Radar3D,RadarBird}]

                        --type LidarBird/Lidar3D/Camera/Radar3D/RadarBird

  --radar [RADAR [RADAR ...]]

                        --radar 1/0

  --video               Will create video of flag raised

  --frame [FRAME [FRAME ...]]

                        --frame int int

  --dataset [DATASET [DATASET ...]]

                        --dataset int

```

### Example

```

python visualization.py --type LidarBird --frame 15 90 --dataset 16 --video

```

## Citation 

```

@inproceedings{bansal2020pointillism,

  title={Pointillism: accurate 3D bounding box estimation with multi-radars},

  author={Bansal, Kshitiz and Rungta, Keshav and Zhu, Siyuan and Bharadia, Dinesh},

  booktitle={Proceedings of the 18th Conference on Embedded Networked Sensor Systems},

  pages={340--353},

  year={2020}

}

```