| | --- |
| | 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)}, |
| | } |
| | ``` |
