Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+tags:
+- image
+annotations_creators:
+- expert-generated
+pretty_name: MVSCPS
+size_categories:
+- n<1K
+task_categories:
+- image-to-3d
+---
+
+# Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo Cues
+
+This dataset contains multi-view One-Light-at-a-Time (OLAT) images captured for multi-view 3D reconstruction and inverse rendering tasks.
+
+It is first introduced and used for qualitative evaluation in our ICCV 2025 paper.
+
+## Dataset Structure
+The dataset contains 6 scenes. Each scene directory is organized as follows:
+
+```
+{capture_date}_{material}_{name}/
+├── ARW/
+├── JPG/
+├── mask/
+├── CAM/
+├── images_overview.jpeg
+└── mesh_RC.obj
+
+```
+
+### File and Folder Descriptions
+
+- 🏞️ **ARW/** and **JPG/**
+
+  Contain raw images in ARW format and corresponding JPEGs captured by a SONY A7R5 camera.
+  No post-processing (e.g., tone mapping or white balance) has been applied.
+  File names follow the format `V{view_idx}L{light_idx}`, where:
+  - `view_idx`: index of the camera view
+  - `light_idx`: index of the light direction
+
+  Images with the same `light_idx` are captured under the same light source **fixed with respect to the camera**.
+
+- ◘ **mask/**  
+  Contains per-view foreground masks, automatically segmented using [SAM2](https://github.com/facebookresearch/sam2).  
+
+- 📸 **CAM/**  
+  Includes camera intrinsic and extrinsic parameters as 3×4 projection matrices $P=K[R \mid t]$ for each view.
+  Calibrated using [RealityCapture](https://www.realityscan.com/en-US) (Now renamed RealityScan).
+
+- 📱**images_overview.jpeg**  
+  Provides a visual overview of all images in a scene.  
+  Each sub-image is cropped from the jpeg image and gamma-corrected for improved visibility.
+
+- 🧱 **mesh_RC.obj**  
+  A reference 3D mesh reconstructed from all JPEG images using [RealityCapture](https://www.realityscan.com/en-US).  
+  ⚠️ **Note:** This mesh is only intended for qualitative reference and should not be used for quantitative evaluation.  
+  Because the photogrammetry process was applied to images captured under varying directional lighting, it violates the photo-consistency assumption and introduce reconstruction artifacts.
+
+
+### Scene Descriptions
+
+- `20250205_ceramic_buddha`
+
+  The first scene captured using the camera-light setup shown in main paper's Fig. 10, and is used in paper's teaser image.
+  143 out of 144 images were successfully calibrated using RealityCapture; thus, only 143 images were used in our method.
+
+- `20250303 ~ 20250304`
+
+  Captured right before ICCV submission deadline for additional qualitative results. Each scene contains 144 successfully calibrated images.
+
+- `20250515_ceramic_buddha`
+
+  Captured during the rebuttal phase to demonstrate that a completely dark room is not strictly required for our method.
+  All images were taken with a 1/200-second exposure under ambient lighting, in contrast to previous scenes captured at 1/8-second exposure in darkness.
+  Because the strobe flashlight emits a short burst of high-intensity light,
+  reducing the exposure time from 1/8 sec to 1/200 sec does not affect the flashlight’s contribution to image appearance.
+  However, it substantially attenuates the influence of ambient light, making it negligible.
+
+  
+#### Data Collection & Attribution
+
+All images were captured, calibrated, segmented, and curated by [Xu Cao](https://xucao-42.github.io/homepage/).
+
+## How to Download
+Step 1: Install the Hugging Face CLI
+```
+pip install -U huggingface_hub
+```
+Step 2: Download the dataset
+```
+huggingface-cli download cyberagent/mvscps --repo-type dataset --local-dir ./mvscps_data
+```
+
+
+## License
+This dataset is available under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License**. 
+You may use, share, and adapt the material for non-commercial purposes with appropriate credit.
+
+## Citation
+If you use this dataset in your work, please cite:
+```
+@inproceedings{mvscps2025cao,
+title = {Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo Cues},
+author = {Cao, Xu and Taketomi, Takafumi},
+year = {2025},
+booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+}
+```