Pi3DET/data

Perspective-Invariant 3D Object Detection

Ao Liang^*,1,2,3,4  Lingdong Kong^*,1  Dongyue Lu^*,1  Youquan Liu⁵  Jian Fang⁴  Huaici Zhao⁴  Wei Tsang Ooi^1
1National University of Singapore    ²University of Chinese Academy of Sciences   
³Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences   
⁴Shenyang Institute of Automation, Chinese Academy of Sciences ⁵Fudan University
^*Equally contributed to this work   

Updates

• [July 2025]: Project page released.
• [June 2025]: Pi3DET has been extended to Track 5: Cross-Platform 3D Object Detection of the RoboSense Challenge at IROS 2025. See the track homepage, GitHub repo for more details.

Todo

Since the Pi3DET dataset is being used for Track 5: Cross-Platform 3D Object Detection of the RoboSense Challenge at IROS 2025, 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.

• Release Phase 1 dataset of the IROS Track, which is KITTI-like single-framee format.
• Release Phase 2 dataset of the IROS Track, which is KITTI-like single-framee format.
• Release all data of Pi3DET, which has temporal information.

Download

The Track 5 dataset follows the KITTI format. Each sample consists of:

• A front-view RGB image
• A LiDAR point cloud covering the camera’s field of view
• Calibration parameters
• 3D bounding-box annotations (for training)

  Calibration and annotations are packaged together in .pkl files.

We use the same training set (vehicle platform) for both phases, but different validation sets. The full dataset is hosted on Hugging Face:

robosense/track5-cross-platform-3d-object-detection

1. Download the dataset

   python tools/load_dataset.py $USER_DEFINE_OUTPUT_PATH
   

2. Link data into the project

    # Create target directory
    mkdir -p data/pi3det
   
    # Link the training split
    ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase12_vehicle_training/training \
        data/pi3det/training
   
    # Link the validation split for Phase 1 (Drone)
    ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase1_drone_validation/validation \
        data/pi3det/validation
   
    # Link the .pkl info files
    ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase12_vehicle_training/training/pi3det_infos_train.pkl \
        data/pi3det/pi3det_infos_train.pkl
    ln -s $USER_DEFINE_OUTPUT_PATH/track5-cross-platform-3d-object-detection/phase1_drone_validation/validation/pi3det_infos_val.pkl \
        data/pi3det/pi3det_infos_val.pkl
   

3. Verify your directory structure
   After linking, your data/ folder should look like this:

    data/
    └── pi3det/
        ├── training/
        │   ├── image/
        │   │   ├── 0000000.jpg
        │   │   └── 0000001.jpg
        │   └── point_cloud/
        │       ├── 0000000.bin
        │       └── 0000001.bin
        ├── validation/
        │   ├── image/
        │   │   ├── 0000000.jpg
        │   │   └── 0000001.jpg
        │   └── point_cloud/
        │       ├── 0000000.bin
        │       └── 0000001.bin
        ├── pi3det_infos_train.pkl
        └── pi3det_infos_val.pkl
   

Pi3DET Dataset

Detailed statistic information

Platform	Condition	Sequence	# of Frames	# of Points (M)	# of Vehicles	# of Pedestrians
Vehicle (8)	Daytime (4)	city_hall	2,982	26.61	19,489	12,199
		penno_big_loop	3,151	33.29	17,240	1,886
		rittenhouse	3,899	49.36	11,056	12,003
		ucity_small_loop	6,746	67.49	34,049	34,346
	Nighttime (4)	city_hall	2,856	26.16	12,655	5,492
		penno_big_loop	3,291	38.04	8,068	106
		rittenhouse	4,135	52.68	11,103	14,315
		ucity_small_loop	5,133	53.32	18,251	8,639
		Summary (Vehicle)	32,193	346.95	131,911	88,986
Drone (7)	Daytime (4)	penno_parking_1	1,125	8.69	6,075	115
		penno_parking_2	1,086	8.55	5,896	340
		penno_plaza	678	5.60	721	65
		penno_trees	1,319	11.58	657	160
	Nighttime (3)	high_beams	674	5.51	578	211
		penno_parking_1	1,030	9.42	524	151
		penno_parking_2	1,140	10.12	83	230
		Summary (Drone)	7,052	59.47	14,534	1,272
Quadruped (10)	Daytime (8)	art_plaza_loop	1,446	14.90	0	3,579
		penno_short_loop	1,176	14.68	3,532	89
		rocky_steps	1,535	14.42	0	5,739
		skatepark_1	661	12.21	0	893
		skatepark_2	921	8.47	0	916
		srt_green_loop	639	9.23	1,349	285
		srt_under_bridge_1	2,033	28.95	0	1,432
		srt_under_bridge_2	1,813	25.85	0	1,463
	Nighttime (2)	penno_plaza_lights	755	11.25	197	52
		penno_short_loop	1,321	16.79	904	103
		Summary (Quadruped)	12,300	156.75	5,982	14,551
All Three Platforms (25)		Summary (All)	51,545	563.17	152,427	104,809

Examples

Examples