Datasets:

Alvin16
/

SpaceSense-Bench

@@ -1,151 +1,138 @@
----
-license: cc-by-nc-4.0
-task_categories:
-  - image-segmentation
-  - 3D-segmentation
-  - object-detection
-  - pose-estimation
-  - depth-estimation
-  - multi-modal fusion
-tags:
-  - spacecraft
-  - satellite
-  - semantic-segmentation
-  - pose-estimation
-  - point-cloud
-  - multi-modal
-  - simulation
-  - space
-  - AirSim
-  - Unreal-Engine
-pretty_name: "SpaceSense-Bench"
-size_categories:
-  - 10K<n<100K
----
-# SpaceSense-Bench: Multi-Modal Spacecraft Perception and Pose Estimation Dataset
-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.
-**Toolkit & Code:** [https://github.com/wuaodi/SpaceSense-Bench](https://github.com/wuaodi/SpaceSense-Bench)
-## 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 |
-## 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 |
-**Test satellites (14):**
-ACE (00), CALIPSO (10), Dawn (20), ExoMars_TGO (30), GRAIL (40), Integral (50), LADEE (60), Lunar_Reconnaissance_Orbiter (70), Mercury_Magnetospheric_Orbiter (80), OSIRIS_REX (90), Proba_2 (100), SOHO (110), Suomi_NPP (120), Ulysses (130)
-**Validation satellites (5):**
-Van_Allen_Probe (131), Venus_Express (132), Voyager (133), WIND (134), XMM_newton (135)
-## 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 |
-## Usage
-### Format Conversion
-Use the toolkit at [https://github.com/wuaodi/SpaceSense-Bench](https://github.com/wuaodi/SpaceSense-Bench) to convert raw data to standard formats:
-```bash
-# Convert to Semantic-KITTI (3D point cloud segmentation)
-python convert/airsim_to_semantickitti.py --raw-data ./data/raw --output ./kitti_data
-# Convert to YOLO (2D object detection)
-python convert/airsim_to_yolo.py --raw-data ./data/raw --output ./yolo_data
-# Convert to MMSegmentation (2D semantic segmentation)
-python convert/airsim_to_mmseg.py --raw-data ./data/raw --output ./mmseg_data
-```
-## License
-This dataset is released under the [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. Non-commercial use only.

+---
+license: cc-by-nc-4.0
+task_categories:
+  - image-segmentation
+  - 3D-segmentation
+  - object-detection
+  - pose-estimation
+  - depth-estimation
+  - multi-modal fusion
+tags:
+  - spacecraft
+  - satellite
+  - semantic-segmentation
+  - pose-estimation
+  - point-cloud
+  - multi-modal
+  - simulation
+  - space
+  - AirSim
+  - Unreal-Engine
+pretty_name: "SpaceSense-Bench"
+size_categories:
+  - 10K<n<100K
+---
+# SpaceSense-Bench: Multi-Modal Spacecraft Perception and Pose Estimation Dataset
+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.
+**Toolkit & Code:** [https://github.com/wuaodi/SpaceSense-Bench](https://github.com/wuaodi/SpaceSense-Bench)
+## 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 |
+## 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 |
+**Test satellites (14):**
+ACE (00), CALIPSO (10), Dawn (20), ExoMars_TGO (30), GRAIL (40), Integral (50), LADEE (60), Lunar_Reconnaissance_Orbiter (70), Mercury_Magnetospheric_Orbiter (80), OSIRIS_REX (90), Proba_2 (100), SOHO (110), Suomi_NPP (120), Ulysses (130)
+**Validation satellites (5):**
+Van_Allen_Probe (131), Venus_Express (132), Voyager (133), WIND (134), XMM_newton (135)
+## 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 |
+## Usage
+### Format Conversion
+Use the toolkit at [https://github.com/wuaodi/SpaceSense-Bench](https://github.com/wuaodi/SpaceSense-Bench) to convert raw data to standard formats
+## License
+This dataset is released under the [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. Non-commercial use only.