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---
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license: mit
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---
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<img src="https://github.com/pi3det/toolkit/blob/main/images/pi3det.gif" width="12.5%"
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align="left">
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# Perspective-Invariant 3D Object Detection
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<p align="center">
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<a href="https://alanliang.vercel.app/" target="_blank">Ao Liang</a><sup>*,1,2,3,4</sup>
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<a href="https://ldkong.com/" target="_blank">Lingdong Kong</a><sup>*,1</sup>
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<a href="https://dylanorange.github.io/" target="_blank">Dongyue Lu</a><sup>*,1</sup>
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<a href="" target="_blank">Youquan Liu</a><sup>5</sup>
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<a href="" target="_blank">Jian Fang</a><sup>4</sup>
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<a href="" target="_blank">Huaici Zhao</a><sup>4</sup>
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<a href="https://www.comp.nus.edu.sg/~ooiwt/" target="_blank">Wei Tsang Ooi</a><sup>1</sup>
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<br />
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<sup>1</sup>National University of Singapore
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<sup>2</sup>University of Chinese Academy of Sciences
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<br />
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<sup>3</sup>Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences
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<br />
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<sup>4</sup>Shenyang Institute of Automation, Chinese Academy of Sciences
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<sup>5</sup>Fudan University
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<br />
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<sup>*</sup>Equally contributed to this work
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</p>
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<p align="center">
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<a href="" target='_blank'>
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<img src="https://img.shields.io/badge/Paper-%F0%9F%93%96-darkred">
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</a>
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<a href="http://pi3det.github.io/" target='_blank'>
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<img src="https://img.shields.io/badge/Project-%F0%9F%94%97-orange">
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</a>
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<a href="" target='_blank'>
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<img src="https://visitor-badge.laobi.icu/badge?page_id=pi3det.Pi3EDT">
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</a>
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</p>
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<img src="https://robosense2025.github.io/images/track5/teaser.png" alt="Teaser" width="100%">
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## Updates
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- **[July 2025]**: Project page released.
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- **[June 2025]**: **Pi3DET** has been extended to <strong>Track 5: Cross-Platform 3D Object Detection</strong> of the <a href="https://robosense2025.github.io/" target="_blank" rel="noopener noreferrer"><strong><u>RoboSense Challenge</u></strong></a> at <a href="https://www.iros25.org/" target="_blank" rel="noopener noreferrer"><strong><u>IROS 2025</u></strong></a>. See the <a href="https://robosense2025.github.io/track5" target="_blank" rel="noopener noreferrer"><strong><u>track homepage</u></strong></a>, <a href="https://github.com/robosense2025/track5" target="_blank" rel="noopener noreferrer"><strong><u>GitHub repo</u></strong></a> for more details.
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## Todo
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> Since the Pi3DET dataset is being used for **Track 5: Cross-Platform 3D Object Detection** of the [**_RoboSense Challenge_**](https://robosense2025.github.io/) at [**_IROS 2025_**](https://www.iros25.org/), in the interest of fairness we are temporarily not releasing all of the data and annotations. If you’re interested, we have open‑sourced a subset of the data and code—please refer to the track details for more information.
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- [x] Release <strong>Phase 1</strong> dataset of the IROS Track, which is KITTI-like single-framee format.
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- [ ] Release <strong>Phase 2</strong> dataset of the IROS Track, which is KITTI-like single-framee format.
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- [ ] Release all data of Pi3DET, which has temporal information.
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## Download
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The Track 5 dataset follows the KITTI format. Each sample consists of:
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- A front-view RGB image
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- A LiDAR point cloud covering the camera’s field of view
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- Calibration parameters
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- 3D bounding-box annotations (for training)
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> Calibration and annotations are packaged together in `.pkl` files.
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We use the **same training set** (vehicle platform) for both phases, but **different validation sets**. The full dataset is hosted on Hugging Face:
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[robosense/track5-cross-platform-3d-object-detection](https://huggingface.co/datasets/robosense/datasets/tree/main/track5-cross-platform-3d-object-detection)
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1. **Download the dataset**
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```bash
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python tools/load_dataset.py $USER_DEFINE_OUTPUT_PATH
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2. **Link data into the project**
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```bash
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# Create target directory
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mkdir -p data/pi3det
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# Link the training split
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ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase12_vehicle_training/training \
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data/pi3det/training
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# Link the validation split for Phase 1 (Drone)
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ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase1_drone_validation/validation \
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data/pi3det/validation
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# Link the .pkl info files
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ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase12_vehicle_training/training/pi3det_infos_train.pkl \
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data/pi3det/pi3det_infos_train.pkl
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ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase1_drone_validation/validation/pi3det_infos_val.pkl \
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data/pi3det/pi3det_infos_val.pkl
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3. **Verify your directory structure**
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After linking, your `data/` folder should look like this:
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```bash
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data/
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└── pi3det/
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├── training/
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│ ├── image/
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│ │ ├── 0000000.jpg
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│ │ └── 0000001.jpg
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│ └── point_cloud/
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│ ├── 0000000.bin
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│ └── 0000001.bin
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├── validation/
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│ ├── image/
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│ │ ├── 0000000.jpg
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│ │ └── 0000001.jpg
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│ └── point_cloud/
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│ ├── 0000000.bin
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│ └── 0000001.bin
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├── pi3det_infos_train.pkl
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└── pi3det_infos_val.pkl
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```
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## Pi3DET Dataset
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### Detailed statistic information
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| Platform | Condition | Sequence | # of Frames | # of Points (M) | # of Vehicles | # of Pedestrians |
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|-----------------------------|----------------|------------------------|------------:|----------------:|--------------:|-----------------:|
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| **Vehicle (8)** | **Daytime (4)**| city_hall | 2,982 | 26.61 | 19,489 | 12,199 |
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| | | penno_big_loop | 3,151 | 33.29 | 17,240 | 1,886 |
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| | | rittenhouse | 3,899 | 49.36 | 11,056 | 12,003 |
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| | | ucity_small_loop | 6,746 | 67.49 | 34,049 | 34,346 |
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| | **Nighttime (4)**| city_hall | 2,856 | 26.16 | 12,655 | 5,492 |
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| | | penno_big_loop | 3,291 | 38.04 | 8,068 | 106 |
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| | | rittenhouse | 4,135 | 52.68 | 11,103 | 14,315 |
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| | | ucity_small_loop | 5,133 | 53.32 | 18,251 | 8,639 |
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| | | **Summary (Vehicle)** | 32,193 | 346.95 | 131,911 | 88,986 |
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| **Drone (7)** | **Daytime (4)**| penno_parking_1 | 1,125 | 8.69 | 6,075 | 115 |
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| | | penno_parking_2 | 1,086 | 8.55 | 5,896 | 340 |
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| | | penno_plaza | 678 | 5.60 | 721 | 65 |
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| | | penno_trees | 1,319 | 11.58 | 657 | 160 |
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| | **Nighttime (3)**| high_beams | 674 | 5.51 | 578 | 211 |
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| | | penno_parking_1 | 1,030 | 9.42 | 524 | 151 |
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| | | penno_parking_2 | 1,140 | 10.12 | 83 | 230 |
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| | | **Summary (Drone)** | 7,052 | 59.47 | 14,534 | 1,272 |
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| **Quadruped (10)** | **Daytime (8)**| art_plaza_loop | 1,446 | 14.90 | 0 | 3,579 |
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| | | penno_short_loop | 1,176 | 14.68 | 3,532 | 89 |
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| | | rocky_steps | 1,535 | 14.42 | 0 | 5,739 |
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| | | skatepark_1 | 661 | 12.21 | 0 | 893 |
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| | | skatepark_2 | 921 | 8.47 | 0 | 916 |
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| | | srt_green_loop | 639 | 9.23 | 1,349 | 285 |
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| | | srt_under_bridge_1 | 2,033 | 28.95 | 0 | 1,432 |
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| | | srt_under_bridge_2 | 1,813 | 25.85 | 0 | 1,463 |
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| | **Nighttime (2)**| penno_plaza_lights | 755 | 11.25 | 197 | 52 |
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| | | penno_short_loop | 1,321 | 16.79 | 904 | 103 |
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| | | **Summary (Quadruped)**| 12,300 | 156.75 | 5,982 | 14,551 |
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| **All Three Platforms (25)**| | **Summary (All)** | 51,545 | 563.17 | 152,427 | 104,809 |
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### Examples
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<img src="https://robosense2025.github.io/images/track5/data_example1.png" alt="Teaser" width="100%">
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<img src="https://robosense2025.github.io/images/track5/data_example2.png" alt="Teaser" width="100%">
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<img src="https://robosense2025.github.io/images/track5/data_example3.png" alt="Teaser" width="100%">
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### Examples
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<img src="https://robosense2025.github.io/images/track5/data_example1.png" alt="Teaser" width="100%">
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<img src="https://robosense2025.github.io/images/track5/data_example2.png" alt="Teaser" width="100%">
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<img src="https://robosense2025.github.io/images/track5/data_example3.png" alt="Teaser" width="100%"> |
