Datasets:

EPFL-CVLab
/

OpenMaterial

Tasks:

Image-to-3D

Size:

1K<n<10K

ArXiv:

Tags:

croissant

Libraries:

License:

Dataset card Files Files and versions Community

Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction

Zheng Dang¹ · Jialu Huang² · Fei Wang² · Mathieu Salzmann¹

¹EPFL CVLAb, Switzerland ² Xi'an Jiaotong University, China

Paper

WebPage

📌 Update log

🗓️ March 2025

Updated degnosie scripts to identify and address rare missing cases caused by server-side cluster fluctuations.
Refined benchmark results for selected algorithms (NeRO, GES, GaussianShader) on the Ablation Dataset.
⚠️ Note: Main benchmark results remain unaffected.
🔗 Updated results available at: [https://christy61.github.io/openmaterial.github.io/]

🗓️ November 2024

Released benchmark results on the Ablation Dataset, with strict control over shape, material, and lighting variables.
Benchmarked a set of representative algorithms across two tasks:
- Novel View Synthesis: Gaussian Splatting, Instant-NGP, 2DGS, PGSR, GES, GSDR, GaussianShader
- 3D Reconstruction: Instant-NeuS, NeuS2, 2DGS, PGSR, NeRO
Updated evaluation scripts to incorporate new algorithms and support the Ablation Dataset benchmarking format.
Improved evaluation code to better visualize benchmarking comparisons.
🔗 Full results available at: [https://christy61.github.io/openmaterial.github.io/]

🗓️ October 2024

Released extended benchmark results on the Main Dataset:
- 7 Novel View Synthesis methods: Gaussian Splatting, Instant-NGP, 2DGS, PGSR, GES, GSDR, GaussianShader
- 6 3D Reconstruction methods: Instant-NeuS, NeuS2, 2DGS, PGSR, NeRO, NeRRF
Highlighted algorithms specialized for challenging materials: NeRO, NeRRF, GSDR, GaussianShader
Updated evaluation scripts to incorporate new algorithms.

🗓️ September 2024

Introduced a new Ablation Dataset for controlled analysis of 3D reconstruction and view synthesis.
Controlled variables:
- Objects: Vase, Snail, Boat, Motor Bike, Statue
- Lighting: Indoor, Daytime Garden, Nighttime Street
- Materials: Conductor， Dielectric Plastic， Rough Conductor， Rough Dielectric， Rough Plastic， Diffuse
Total: 105 unique scenes (5 × 3 × 7)
🔗 Data is now available.

🗓️ July 2024

Dataset restructured for flexible material-type-based downloading.
Users can now download subsets of data focusing on specific material categories (e.g., diffuse, conductor, dielectric, plastic).
📦 Updated download scripts included.

🗓️ May 2024

Released OpenMaterial, a semi-synthetic dataset featuring:
- 1001 unique shapes, 295 materials with lab-measured IOR spectra
- 723 lighting conditions
- High-res images (1600×1200), camera poses, depth, 3D models, masks
- Stored in standard COLMAP format
Released a new benchmark including a novel evaluation dimension: material type
Benchmarked methods: Instant-NeuS, NeuS2, Gaussian Splatting, Instant-NGP

Dataset

[+] 1001 unique shapes

[+] 295 material types with laboratory measured IOR

[+] 723 lighting conditions

[+] Physical based rendering with costomized BSDF for each material type

[+] 1001 uniques scenes, for each scene 90 images (50 for training, 40 for testing) with object mask, depth, camera pose, materail type annotations.

Example Images

Data structure

.
├── name_of_object/[lighing_condition_name]-[material_type]-[material_name]
│   ├── train
│   │   ├── images
│   │   │   ├── 000000.png
│   │   │   |-- ...
│   │   └── mask
│   │   │   ├── 000000.png
│   │   │   |-- ...
│   │   └── depth
│   │       ├── 000000.png
│   │       |-- ...
│   ├── test
│   │   ├── images
│   │   │   ├── 000000.png
│   │   │   |-- ...
│   │   └── mask
│   │   │   ├── 000000.png
│   │   │   |-- ...
│   │   └── depth
│   │       ├── 000000.png
│   │       |-- ...
│   └── transformas_train.json
│   └── transformas_test.json

Usage

Check out our Example Code for implementation details!

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