Add task categories, paper link, and toolkit usage (#2)

- Add task categories, paper link, and toolkit usage (31cadef33770ba97ec03be057e67650f1a54dbcc)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,20 +1,29 @@
 ---
 license: cc-by-nc-4.0
-pretty_name: "SpaceSense-Bench"
 size_categories:
-  - 70GB
+- 10GB<n<100GB
+pretty_name: SpaceSense-Bench
+task_categories:
+- object-detection
+- image-segmentation
+- depth-estimation
+- other
+tags:
+- space
+- satellite
+- multi-modal
+- lidar
+- pose-estimation
 ---
 
 # 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)
+[**Project Page**](https://wuaodi.github.io/SpaceSense-Bench/) | [**Paper**](https://huggingface.co/papers/2603.09320) | [**Toolkit & Code**](https://github.com/wuaodi/SpaceSense-Bench)
 
+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.
 
 ![teaser](https://cdn-uploads.huggingface.co/production/uploads/65a5e3a86145ebc6e7fefb20/e8ehYJXlWgR7vhq7LDLwM.png)
 
-
 ## Dataset Overview
 
 | Item | Detail |
@@ -26,6 +35,32 @@ A high-fidelity simulation-based multi-modal(RGB, Depth, LiDAR Point Cloud) data
 | 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](https://github.com/wuaodi/SpaceSense-Bench/tree/main/SpaceSense-Toolkit) provides tools for converting raw data to standard formats and visualizing the results.
+
+### Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+### Conversion and Visualization
+
+```bash
+# 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 |
@@ -76,12 +111,6 @@ The training and validation sets contain **completely non-overlapping satellite
 | 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:
@@ -99,7 +128,6 @@ Each `.tar.gz` file in the `raw/` folder contains data for one satellite:
 └── ...
 ```
 
-
 ## Semantic Class Definitions
 
 | Class ID | Name | Description |
@@ -113,12 +141,17 @@ Each `.tar.gz` file in the `raw/` folder contains data for one satellite:
 | 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.
+
+## Citation
+
+```bibtex
+@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}
+}
+```