README.md

---
configs:
  - config_name: default

license: cc-by-nc-4.0
tags:
  - croissant
size_categories:
  - 1K<n<10K
task_categories:
  - image-to-3d
---

# OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction
 
 Zheng Dang<sup>1</sup>  ·  Jialu Huang<sup>2</sup>  ·  Fei Wang<sup>2</sup>  ·  Mathieu Salzmann<sup>1</sup>

 <sup>1</sup>EPFL CVLAb, Switzerland <sup>2</sup> Xi'an Jiaotong University, China
 

[Paper](https://arxiv.org/abs/2406.08894)              

[WebPage](https://christy61.github.io/openmaterial.github.io/)

<img src="https://cdn-uploads.huggingface.co/production/uploads/665def1b1d30854dbbde3e87/PBaPM9PAickSO8LnmWF9z.png" width="92%"/>

---
## **📌 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

<div style="display: flex; align-items: flex-start; justify-content: flex-start; gap:2%;">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/638884d65588554e2425e625/dlFmsdbJqFKnDUN3yg_S1.png" style="width:40%;" alt="Example 1"/>
  <img src="https://cdn-uploads.huggingface.co/production/uploads/638884d65588554e2425e625/A9mmqEVW_3BgMWey5cPrC.png" style="width:40%;" alt="Example 2"/>
</div>
<div style="display: flex; align-items: flex-start; justify-content: flex-start; gap:2%; margin-top:-2em;">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/638884d65588554e2425e625/1k_zGTTZAYyJtcIDo0FOO.png" style="width:40%;" alt="Example 3"/>
  <img src="https://cdn-uploads.huggingface.co/production/uploads/638884d65588554e2425e625/w5P_MvlTXt6FMwEDMwPwe.png" style="width:40%;" alt="Example 4"/>
</div>


## 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`](https://github.com/Christy61/OpenMaterial) for implementation details!

<!-- ## Citation

If you find our work useful in your research, please cite:

```
@article{Dang24,
	title={OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction},
	author={Zheng Dang and Jialu Huang and Fei Wang and Mathieu Salzmann},
	journal={arXiv preprint arXiv:2406.08894},
	year={2024}
}
 -->

```