dataset card

README.md

@@ -1,102 +1,119 @@
 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
-{}
+language:
+- en
+license: MIT?????
+tags:
+- {novel view synthesis}  # Example: audio
+- {inverse rendering}  # Example: bio
+- {material decomposition}  # Example: natural-language-understanding
+annotations_creators:
+- {expert-generated}  # Example: crowdsourced, found, expert-generated, machine-generated
+pretty_name: {OpenIllumination}  # Example: SQuAD
+size_categories:
+- {100K<n<1M}  # Example: n<1K, 100K<n<1M, …
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- {other}
+download_size: 900G???
+#paperswithcode_id: {paperswithcode_id}  # Dataset id on PapersWithCode (from the URL). #Example for SQuAD: squad
+#configs:  # Optional for datasets with multiple configurations like glue.
+#- {config_0}  # Example for glue: sst2
+#- {config_1}  # Example for glue: cola
 ---
 
-# Dataset Card for Dataset Name
+# Dataset Card for OpenIllumination
 
 ## Dataset Description
 
-- **Homepage:** 
-- **Repository:** 
-- **Paper:** 
-- **Leaderboard:** 
-- **Point of Contact:** 
+- **Homepage:** https://annonymous2023neuripsdataset.github.io/
+- **Repository:** N/A for now.
+- **Paper:** N/A for now.
+- **Leaderboard:** N/A for now.
+- **Point of Contact:** lal005@ucsd.edu, lic032@ucsd.edu, haosu@ucsd.edu
 
 ### Dataset Summary
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+Our dataset comprises 64 objects, each captured from 70 views, under 13 lighting patterns and 142 One-Light-At-Time (OLAT) illumination, respectively. 
+The 70 views are captured by 48 DSLR cameras and 22 high-speed cameras.
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+* Novel view synthesis: The dataset can be used to evaluate NVS methods, such as NeRF, TensoRF, and NeuS.
+* Inverse rendering: The dataset can be used to evaluate inverse rendering algorithms, which is to decompose illumination, object geometry, and object materials.
 
 ### Languages
 
-[More Information Needed]
+English
 
 ## Dataset Structure
 
-### Data Instances
-
-[More Information Needed]
-
 ### Data Fields
 
-[More Information Needed]
+For each image, the following fields are provided:
+
+* file_path: str, the file path to an image.
+* light_idx: int, the index of illuminations, from 1 to 13 for lighting patterns, or from 0 to 141 for OLAT.
+* transform_matrix: list, a 4x4 matrix, representing the camera pose for this image (in OpenCV convention).
+* camera_angle_x: float, can be used to compute the corresponding camera intrinsics.
+* obj_mask: the object mask, can be read by ```imageio.imread(OBJ_MASK_PATH)>0```, used for PSNR evaluation.
+* com_mask (optional): the union of the object mask and the support mask, can be read by ```imageio.imread(COM_MASK_PATH)>0```, used for t
 
 ### Data Splits
 
-[More Information Needed]
+The data is split into training and testing views. For each object captured under 13 lighting patterns, the training set and the testing set contain 38 and 10 views, respectively. For each object captured under OLAT, the training set and the testing set contain 17 and 5 views, respectively.
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed]
+From the paper:
+
+> Recent efforts have introduced some datasets that incorporate multiple illuminations in real-world settings. However, most of them are limited either in the number of views or the number of illuminations; few of them provide object-level data as well. Consequently, these existing datasets prove unsuitable for evaluating inverse rendering methods on real-world objects.
+>
+>
+> To address this, we present a new dataset containing objects with a variety of materials, captured under multiple views and illuminations, allowing for reliable evaluation of various inverse rendering tasks with real data.
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+From the paper:
 
-#### Who are the source language producers?
-
-[More Information Needed]
+> Our dataset was acquired using a setup similar to a traditional light stage, where densely distributed cameras and controllable lights are attached to a static frame around a central platform.
 
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
+From the paper:
 
-### Personal and Sensitive Information
+> To obtain high-quality segmentation masks, we propose to use Segment-Anything (SAM) to perform instance segmentation. However, 
+> we find that the performance is not satisfactory. One reason is that the object categories are highly undefined. In this case, even combining the bounding box and point prompts cannot produce satisfactory results. To address this problem, we propose to use multiple bounding-box prompts to perform segmentation for each possible part and then calculate a union of the masks as the final object mask.
+>
+> For objects with very detailed and thin structures, e.g. hair, we use an off-the-shelf background matting method to perform object segmentation.
 
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
+#### Who are the annotators?
 
-[More Information Needed]
+Linghao Chen, Isabella Liu, and Ziyang Fu.
 
 ## Additional Information
 
 ### Dataset Curators
 
-[More Information Needed]
+Isabella Liu, Linghao Chen, Ziyang Fu, Liwen Wu, Haian Jin, Zhong Li, Chin Ming Ryan Wong, Yi Xu, Ravi Ramamoorthi, Zexiang Xu and Hao Su
 
 ### Licensing Information
 
-[More Information Needed]
+Non-commercial use only????????
 
 ### Citation Information
 
-[More Information Needed]
+```bash
+@article{liu2023openillumination,
+	title={OpenIllumination: A Multi-Illumination Dateset for Inverse Rendering Evaluation on Real Objects},
+	author={Liu, Isabella and Chen, Linghao and Fu, Ziyang and Wu, Liwen and Jin, Haian and Li, Zhong and Chin Ming Ryan Wong3 and Xu, Yi and Ravi Ramamoorthi1 and Xu, Zexiang and Su, Hao},
+	year={2023}
+}
+```
 
 ### Contributions