dataset

README.md

@@ -22,8 +22,6 @@ tags:
 
 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.
 
-> **Update (2026-05-19).** Following [issue #5](https://github.com/wuaodi/SpaceSense-Bench/issues/5), the `pose_ground_truth.csv` for all 136 spacecraft has been regenerated to fix a frame-timing misalignment with the rendered sensor data. The RGB / depth / segmentation / lidar files themselves are unchanged. All `raw/<sat>.tar.gz` archives have been re-uploaded with the corrected pose CSVs.  Thanks to @zstar239 for the original report.
-
 ![teaser](https://cdn-uploads.huggingface.co/production/uploads/65a5e3a86145ebc6e7fefb20/e8ehYJXlWgR7vhq7LDLwM.png)
 
 ## Dataset Overview
@@ -67,34 +65,33 @@ python SpaceSense-Toolkit/convert/airsim_to_yolo.py --raw-data data_example --ou
 
 | Modality | Format | Unit / Range | Description |
 |----------|--------|-------------|-------------|
-| RGB | PNG (1024x1024) | 8-bit color | Scene rendering from `cam0` |
-| Depth | NPZ (compressed) | int32, millimeters (`depth` key); background = 10,000 m | Per-pixel metric depth exported from AirSim `DepthPerspective` |
-| Semantic Segmentation | PNG (1024x1024, RGB color mask) | color-coded part labels | Component-level segmentation mask; toolkit maps colors to 7 semantic classes |
-| 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) | Target-spacecraft pose in camera frame, plus service/camera pose in world frame |
+| 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 (FRD, right-handed) |
+| 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), stored as compressed `.npz`; deep-space background = 10,000 m |
-| LiDAR | meters (m), `.asc` format (`x,y,z`), 3 decimal places |
-| Timestamp | `YYYYMMDDHHMMSSmmm` |
+| 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`)
+### Camera (cam0)
 
 - Resolution: 1024 x 1024
 - FOV: 50 degrees
-- Image types captured: RGB (`Scene`, type 0), Segmentation (type 5), Depth (`DepthPerspective`, type 2)
+- Image types captured: RGB (type 0), Segmentation (type 5), Depth (type 2)
 - TargetGamma: 1.0
-- Mounting offset: 1 m in front of the service spacecraft body origin
 
 ### LiDAR
 
@@ -102,7 +99,7 @@ python SpaceSense-Toolkit/convert/airsim_to_yolo.py --raw-data data_example --ou
 - Channels: 256
 - Vertical FOV: -20 to +20 degrees
 - Horizontal FOV: -20 to +20 degrees
-- Data frame: `SensorLocalFrame`
+- Data frame: SensorLocalFrame
 
 ## Data Split (Zero-shot / OOD)
 
@@ -116,34 +113,21 @@ The training and validation sets contain **completely non-overlapping satellite
 
 ## Data Organization
 
-Each `.tar.gz` file in the `raw/` folder contains data for one satellite. The toolkit expects trajectory folders containing `image/`, `seg/`, `depth/`, `lidar/`, and `pose_ground_truth.csv`.
-
-Typical layout:
+Each `.tar.gz` file in the `raw/` folder contains data for one satellite:
 
-```text
-<satellite_name>/ or <timestamp>_<satellite_name>/
+```
+<timestamp>_<satellite_name>/
 ├── approach_front/
-│   ├── image/              # RGB images (.png)
-│   ├── depth/              # Depth maps (.npz, int32, mm, key = depth)
-│   ├── seg/                # Semantic masks (.png, RGB color encoding)
-│   ├── lidar/              # Point clouds (.asc)
-│   └── pose_ground_truth.csv
+│   ├── 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/
 └── ...
 ```
 
-Some HuggingFace exports may contain one additional nesting level:
-
-```text
-<satellite_name>/
-└── <satellite_name>/
-    ├── approach_front/
-    ├── approach_back/
-    ├── orbit_xy/
-    └── ...
-```
-
 ## Semantic Class Definitions
 
 | Class ID | Name | Description |
@@ -170,4 +154,4 @@ This dataset is released under the [CC-BY-NC-4.0](https://creativecommons.org/li
 	year={2026},
 	url={https://arxiv.org/abs/2603.09320}
 }
-```
+```

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