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README.md

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+# 🕯️ Light-Stage OLAT Subsurface-Scattering Dataset
+
+*Companion data for the paper **"Subsurface Scattering for 3D Gaussian Splatting"***
+
+> **This README documents *only the dataset*.**  
+> A separate repo covers the training / rendering **code**: <https://github.com/cgtuebingen/SSS-GS>
+
+<p align="center">
+  <img src="other/dataset.png" width="80%" alt="Dataset overview"/>
+</p>
+
+## Overview
+
+Subsurface scattering (SSS) gives translucent materials (wax, soap, jade, skin) their distinctive soft glow. Our paper introduces **SSS-GS**, the first 3D Gaussian-Splatting framework that *jointly* reconstructs shape, BRDF and volumetric SSS while running at real-time framerates. Training such a model requires dense **multi-view ⇄ multi-light OLAT** data.
+
+This dataset delivers exactly that:
+
+* **25 objects** – 20 captured on a physical light-stage, 5 rendered in a synthetic stage
+* **> 37k images** (≈ 1 TB raw / ≈ 30 GB processed) with **known camera & light poses**
+* Ready-to-use JSON transform files compatible with NeRF & 3D GS toolchains
+* Processed to 800 px images + masks; **raw 16 MP capture** available on request
+
+### Applications
+
+* Research on SSS, inverse-rendering, radiance-field relighting, differentiable shading
+* Benchmarking OLAT pipelines or light-stage calibration
+* Teaching datasets for photometric 3D reconstruction
+
+## Quick Start
+
+```bash
+# Download and extract one real-world object
+curl -L https://…/real_world/candle.tar | tar -x
+```
+
+## Directory Layout
+```
+dataset_root/
+├── real_world/          # Captured objects (processed, ready to train)
+│   └── <object>.tar     # Each tar = one object (≈ 4–8 GB)
+└── synthetic/           # Procedurally rendered objects
+    ├── <object>_full/   # full-resolution (800 px)
+    └── <object>_small/  # 256 px "quick-train" version
+```
+
+### Inside a **real-world** tar
+```
+<object>/
+├── resized/                 # θ_φ_board_i.png  (≈ 800 × 650 px)
+├── transforms_train.json    # (train-set only) ⇄  camera / light metadata
+├── transforms_test.json     # (test-set only) ⇄  camera / light metadata
+├── light_positions.json     # all θ_φ_board_i → (x,y,z)
+├── exclude_list.json        # bad views (lens flare, matting error, …)
+└── cam_lights_aligned.png   # sanity-check visualisation
+```
+*Raw capture* Full-resolution, unprocessed RGB-bayer images (~ 1 TB per object) are kept offline—contact us to arrange transfer.
+
+### Inside a **synthetic** object folder
+```
+<object>_full/
+├── <object>.blend         # Blender scene with 112 HDR stage lights
+├── train/                 # r_<cam>_l_<light>.png (= 800 × 800 px)
+├── test/                  # r_<cam>_l_<light>.png (= 800 × 800 px)
+├── eval/                  # only in "_small" subsets
+├── transforms_train.json  # (train-set only) ⇄  camera / light metadata
+└── transforms_test.json   # (test-set only) ⇄  camera / light metadata
+```
+The *small* variant differs only in image resolution & optional `eval/`.
+
+## Data Collection
+
+### Real-World Subset
+
+**Capture Setup:**
+- **Stage**: 4 m diameter light-stage with 167 individually addressable LEDs
+- **Camera**: FLIR Oryx 12 MP with 35 mm F-mount, motorized turntable & vertical rail
+- **Processing**: COLMAP SfM, automatic masking (SAM + ViTMatte), resize → PNG
+
+| Objects | Avg. Views | Lights/View | Resolution | Masks |
+|---------|------------|-------------|------------|-------|
+| 20      | 158        | 167         | 800×650 px | α-mattes |
+
+<p align="center">
+  <img src="other/preprocessing.png" width="60%" alt="Preprocessing pipeline"/>
+</p>
+
+### Synthetic Subset
+
+**Rendering Setup:**
+- **Models**: Stanford 3D Scans and BlenderKit
+- **Renderer**: Blender Cycles with spectral SSS (Principled BSDF)
+- **Lights**: 112 positions (7 rings × 16), 200 test cameras on NeRF spiral path
+
+| Variant | Images | Views × Lights | Resolution | Notes |
+|---------|--------|----------------|------------|-------|
+| _full   | 11,200 | 100 × 112      | 800²       | Filmic tonemapping |
+| _small  | 1,500  | 15 × 100       | 256²       | Quick prototyping |
+
+## File & Naming Conventions
+* **Real images** `theta_<θ>_phi_<φ>_board_<id>.png`  
+  *θ, φ* in degrees; *board* 0-195 indexes the LED PCBs.  
+* **Synthetic images** `r_<camera>_l_<light>.png`  
+* **JSON schema**  
+  ```jsonc
+  {
+    "camera_angle_x": 0.3558,
+    "frames": [{
+      "file_paths": ["resized/theta_10.0_phi_0.0_board_1", …],
+      "light_positions": [[x,y,z], …],   // metres, stage origin
+      "transform_matrix": [[...], ...],  // 4×4 extrinsic
+      "width": 800, "height": 650, "cx": 400.0, "cy": 324.5
+    }]
+  }
+  ```
+  For synthetic files: identical structure, naming `r_<cam>_l_<light>`.
+
+## Licensing & Third-Party Assets
+| Asset | Source | License / Note |
+|-------|--------|----------------|
+| Synthetic models | [Stanford 3-D Scans](https://graphics.stanford.edu/data/3Dscanrep/) | Varies (non-commercial / research) |
+|                  | [BlenderKit](https://www.blenderkit.com/) | CC-0, CC-BY or Royalty-Free (check per-asset page) |
+| HDR env-maps     | [Poly Haven](https://polyhaven.com/) | CC-0 |
+| Code             | MIT (see repo) |
+
+The dataset is released **for non-commercial research and educational use**.  
+If you plan to redistribute or use individual synthetic assets commercially, verify the upstream license first.
+
+## Citation
+If you use this dataset, please cite the paper:
+
+```bibtex
+@inproceeding{sss_gs,
+ author = {Dihlmann, Jan-Niklas and Majumdar, Arjun and Engelhardt, Andreas and Braun, Raphael and Lensch, Hendrik P.A.},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
+ pages = {121765--121789},
+ publisher = {Curran Associates, Inc.},
+ title = {Subsurface Scattering for Gaussian Splatting},
+ url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/dc72529d604962a86b7730806b6113fa-Paper-Conference.pdf},
+ volume = {37},
+ year = {2024}
+}
+
+```
+
+## Contact & Acknowledgements
+Questions, raw-capture requests, or pull-requests?  
+📧 `jan-niklas.dihlmann (at) uni-tuebingen.de`
+
+This work was funded by DFG (EXC 2064/1, SFB 1233) and the Tübingen AI Center.