SpaceSense-Bench: Multi-Modal Spacecraft Perception and Pose Estimation Dataset

Project Page | Paper | Toolkit & Code

SpaceSense-Bench is a high-fidelity simulation-based multi-modal (RGB, Depth, LiDAR Point Cloud) dataset for spacecraft component-level semantic understanding, containing 136 satellite models with synchronized multi-modal data.

Dataset Overview

Item	Detail
Satellite Models	136 (sourced from NASA/ESA 3D models)
Data Modalities	RGB, Depth, Semantic Segmentation, LiDAR Point Cloud, 6-DoF Pose
Image Resolution	1024 x 1024
Camera FOV	50 degrees
Semantic Classes	7 (main_body, solar_panel, dish_antenna, omni_antenna, payload, thruster, adapter_ring)
Simulation Platform	Unreal Engine 5.2.0 + AirSim 1.8.1

Sample Usage

The SpaceSense-Toolkit provides tools for converting raw data to standard formats and visualizing the results.

Installation

pip install -r requirements.txt

Conversion and Visualization

# Visualize the raw data
python SpaceSense-Toolkit/visualize/raw_data_web_visualizer.py --raw-data data_example

# Convert to Semantic-KITTI (3D segmentation)
python SpaceSense-Toolkit/convert/airsim_to_semantickitti.py --raw-data data_example --output output/semantickitti --satellite-json SpaceSense-Toolkit/configs/satellite_descriptions.json

# Convert to MMSegmentation (2D segmentation)
python SpaceSense-Toolkit/convert/airsim_to_mmseg.py --raw-data data_example --output output/mmseg

# Convert to YOLO (Object detection)
python SpaceSense-Toolkit/convert/airsim_to_yolo.py --raw-data data_example --output output/yolo

Data Modalities

Modality	Format	Unit / Range	Description
RGB	PNG (1024x1024)	8-bit color	Scene rendering
Depth	PNG (1024x1024)	int32, millimeters (0 ~ 10,000,000 mm, background = 10,000 m)	Per-pixel depth map
Semantic Segmentation	PNG (1024x1024)	uint8, class ID per pixel (0 = background)	Component-level segmentation mask
LiDAR Point Cloud	ASC (x y z per line)	meters, 3 decimal places	Sparse 3D point cloud
6-DoF Pose	CSV	meters + Hamilton quaternion (w,x,y,z)	Camera-to-target relative pose

Coordinate System & Units

Item	Convention
Camera Frame	X-forward, Y-right, Z-down (right-hand system)
World Frame	AirSim NED, target spacecraft fixed at origin
Quaternion	Hamilton convention: w + xi + yj + zk
Euler Angles	ZYX intrinsic (Yaw-Pitch-Roll)
Position	meters (m), 6 decimal places
Depth Map	millimeters (mm), int32; deep space background = 10,000 m
LiDAR	meters (m), .asc format (x y z), 3 decimal places
Timestamp	YYYYMMDDHHMMSSmmm

Sensor Configuration

Camera (cam0)

• Resolution: 1024 x 1024
• FOV: 50 degrees
• Image types captured: RGB (type 0), Segmentation (type 5), Depth (type 2)
• TargetGamma: 1.0

LiDAR

• Range: 300 m
• Channels: 256
• Vertical FOV: -20 to +20 degrees
• Horizontal FOV: -20 to +20 degrees
• Data frame: SensorLocalFrame

Data Split (Zero-shot / OOD)

The training and validation sets contain completely non-overlapping satellite models, so validation performance reflects zero-shot generalization to unseen spacecraft.

Split	Satellites	Rule
Train	117	All satellites excluding val and excluded
Test	14	Every 10th by index: seq 00, 10, 20, ..., 130
Validation	5	Seq 131-135, reserved for future testing

Data Organization

Each .tar.gz file in the raw/ folder contains data for one satellite:

<timestamp>_<satellite_name>/
├── approach_front/
│   ├── rgb/              # RGB images (.png)
│   ├── depth/            # Depth maps (.png, int32, mm)
│   ├── segmentation/     # Semantic masks (.png, uint8)
│   ├── lidar/            # Point clouds (.asc)
│   └── poses.csv         # 6-DoF poses
├── approach_back/
├── orbit_xy/
└── ...

Semantic Class Definitions

Class ID	Name	Description
0	background	Deep space background
1	main_body	Spacecraft main body / bus
2	solar_panel	Solar panels / solar arrays
3	dish_antenna	Dish / parabolic antennas
4	omni_antenna	Omnidirectional antennas / booms
5	payload	Scientific instruments / payloads
6	thruster	Thrusters / propulsion systems
7	adapter_ring	Launch adapter rings

License

This dataset is released under the CC-BY-NC-4.0 license. Non-commercial use only.

Citation

@article{SpaceSense-Bench,
    title={SpaceSense-Bench: A Large-Scale Multi-Modal Benchmark for Spacecraft Perception and Pose Estimation},
    author={Aodi Wu, Jianhong Zuo, Zeyuan Zhao, Xubo Luo, Ruisuo Wang, Xue Wan},
    year={2026},
    url={https://arxiv.org/abs/2603.09320}
}