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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- image |
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annotations_creators: |
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- expert-generated |
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pretty_name: MVSCPS |
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size_categories: |
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- n<1K |
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task_categories: |
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- image-to-3d |
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--- |
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# Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo Cues |
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This dataset contains multi-view One-Light-at-a-Time (OLAT) images captured for multi-view 3D reconstruction and inverse rendering tasks. |
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It is first introduced and used for qualitative evaluation in our [ICCV 2025 paper](https://www.arxiv.org/abs/2507.23162). |
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## Dataset Structure |
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The dataset contains 6 scenes. Each scene directory is organized as follows: |
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``` |
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{capture_date}_{material}_{name}/ |
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├── ARW/ |
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├── JPG/ |
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├── mask/ |
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├── CAM/ |
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├── images_overview.jpeg |
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└── mesh_RC.obj |
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``` |
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### File and Folder Descriptions |
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- 🏞️ **ARW/** and **JPG/** |
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Contain raw images in ARW format and corresponding JPEGs captured by a SONY A7R5 camera. |
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No post-processing (e.g., tone mapping or white balance) has been applied. |
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File names follow the format `V{view_idx}L{light_idx}`, where: |
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- `view_idx`: index of the camera view |
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- `light_idx`: index of the light direction |
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Images with the same `light_idx` are captured under the same light source **fixed with respect to the camera**. |
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- ◘ **mask/** |
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Contains per-view foreground masks, automatically segmented using [SAM2](https://github.com/facebookresearch/sam2). |
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- 📸 **CAM/** |
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Includes camera intrinsic and extrinsic parameters as 3×4 projection matrices $P=K[R \mid t]$ for each view. |
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Calibrated using [RealityCapture](https://www.realityscan.com/en-US) (Now renamed RealityScan). |
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- 📱**images_overview.jpeg** |
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Provides a visual overview of all images in a scene. |
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Each sub-image is cropped from the JPEG image and gamma-corrected for improved visibility. |
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- 🧱 **mesh_RC.obj** |
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A reference 3D mesh reconstructed from all JPEG images using [RealityCapture](https://www.realityscan.com/en-US). |
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⚠️ **Note:** This mesh is only intended for qualitative reference and should not be used for quantitative evaluation. |
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Because the photogrammetry process was applied to images captured under varying directional lighting, it violates the photo-consistency assumption and introduce reconstruction artifacts. |
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### Scene Descriptions |
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- `20250205_ceramic_buddha` |
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The first scene captured using the camera-light setup shown in the main paper's Fig. 10, and is used in the paper's teaser image. |
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143 out of 144 images were successfully calibrated using RealityCapture; thus, only 143 images were used in our method. |
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- `20250303 ~ 20250304` |
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Captured right before the ICCV submission deadline for additional qualitative results. Each scene contains 144 successfully calibrated images. |
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- `20250515_ceramic_buddha` |
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Captured during the rebuttal phase to demonstrate that a completely dark room is not strictly required for our method. |
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All images were taken with a 1/200-second exposure under ambient lighting, in contrast to previous scenes captured at a 1/8-second exposure in darkness. |
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Because the strobe flashlight emits a short burst of high-intensity light, |
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reducing the exposure time from 1/8 sec to 1/200 sec does not affect the flashlight’s contribution to image appearance. |
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However, it substantially attenuates the influence of ambient light, making it negligible. |
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#### Data Collection & Attribution |
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All images were captured, calibrated, segmented, and curated by [Xu Cao](https://xucao-42.github.io/homepage/). |
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## How to Download |
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Step 1: Install the Hugging Face CLI |
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``` |
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pip install -U huggingface_hub |
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``` |
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Step 2: Download the dataset |
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``` |
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hf download cyberagent/mvscps --repo-type dataset --local-dir ./mvscps_data |
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``` |
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## License |
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This dataset is available under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License**. |
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You may use, share, and adapt the material for non-commercial purposes with appropriate credit. |
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## Citation |
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If you use this dataset in your work, please cite: |
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``` |
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@inproceedings{mvscps2025cao, |
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title = {Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo Cues}, |
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author = {Cao, Xu and Taketomi, Takafumi}, |
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year = {2025}, |
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booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
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} |
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``` |
