Datasets:

vLAR
/

LavalObjaverseDataset

---
language:
- en
tags:
- relighting
- multi-view
- 3d
- objaverse
- hdr
- rendering
- depth-maps
task_categories:
- image-to-image
- depth-estimation
license: cc-by-4.0
size_categories:
- 10M<n<100M
---
<p align="center">
  <svg width="100%" height="100" viewBox="0 0 1000 100" xmlns="http://www.w3.org/2000/svg">
    <defs>
      <!-- 定义蓝-紫-粉的科技风渐变 -->
      <linearGradient id="gradientText" x1="0%" y1="0%" x2="100%" y2="0%">
        <stop offset="0%" style="stop-color:#00c6ff;stop-opacity:1" />
        <stop offset="50%" style="stop-color:#9d50bb;stop-opacity:1" />
        <stop offset="100%" style="stop-color:#ff00cc;stop-opacity:1" />
      </linearGradient>
    </defs>
    <text x="50%" y="55%" font-family="Arial, sans-serif" font-size="75" font-weight="900" fill="url(#gradientText)" text-anchor="middle" dominant-baseline="middle" letter-spacing="2">Laval Objaverse Dataset</text>
  </svg>
</p>
<p align="center">
  <img src="https://raw.githubusercontent.com/vLAR-group/RelightFormer/main/demo/teaser.jpg" alt="Teaser" width="80%">
</p>

<p align="center">
  <strong>vLAR Group</strong> | <em>SIGGRAPH Asia 2026</em>
</p>

<p align="center">
  <a href="https://github.com/vLAR-group/RelightFormer">
    <img src="https://img.shields.io/badge/GitHub-Repository-181717?logo=github" alt="GitHub">
  </a>
    <a href="https://arxiv.org/abs/2609.07414"><img src="https://img.shields.io/badge/arXiv-2604.07414-b31b1b.svg" alt="arXiv"></a>
    <a href="https://huggingface.co/datasets/vLAR/LavalObjaverseDataset"><img src="https://img.shields.io/badge/🤗-Dataset-yellow" alt="Dataset"></a>
    <a href="https://huggingface.co/vLAR/RelightFormer"><img src="https://img.shields.io/badge/🤗-Model-yellow" alt="Model"></a>
    <a href="#license"><img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg" alt="License"></a>
  </p>
<p align="center">
  <strong>A large-scale, high-quality dataset for multi-view relighting.</strong>
</p>

---

## 📖 Dataset Summary

The **Laval Objaverse Dataset** is a comprehensive dataset designed for multi-view relighting and novel view synthesis tasks. It combines high-quality 3D assets from Objaverse with realistic, diverse illumination conditions from the Laval Indoor and Outdoor HDR datasets. Each render includes synchronized multi-view images, depth maps, and complete lighting metadata.

## 🏗️ Dataset Construction

We structured our dataset through a rigorous four-step pipeline:

1. **🎯 Object Filtering:**  
   We source base meshes from the [Objaverse](https://objaverse.allenai.org/) dataset. To ensure high visual fidelity, we exclude meshes with poor geometry or materials by adopting the strict object selection criteria from the relitObjaverse dataset [[1]](#references).

2. **💡 Lighting Selection:**  
   We employ open-source illumination datasets, specifically the *Laval Indoor HDR Dataset* [[2]](#references) and the *Laval Outdoor HDR Dataset* [[3]](#references). Each illumination map is uniformly rotated 16 times to maximize lighting diversity. For every object, we sample 16 environment maps with a balanced 1:1 ratio of indoor and outdoor lighting conditions.

3. **📷 Rendering Protocol:**  
   All objects are normalized to unit size (largest bounding box dimension = 1) and placed at the origin. Cameras are uniformly distributed on a sphere at distances of 1.8 or 2.2 units from the center, oriented toward the scene center.

4. **🛡️ Dataset Partitioning:**  
   The dataset is split into **training, validation, and test sets** with strict disjointness: no camera poses, illumination conditions, or 3D objects overlap between splits. The division information are under **`info/laval`**, **`info/objaverse`** and **`info/view`** for illuminations, objects and viewpoints respecitively.

## 📂 Dataset Structure

```text
LavalObjaverseDataset/
└── info/
│   ├── laval/
│   │   └── full_testing_lighting.json
│   │   └── full_training_lighting.json
│   │   └── full_validation_lighting.json
│   │ 
│   ├── objaverse/
│   │   └── all_objects.json
│   │   └── full_{split}_objects.json # split = testing, training, validation
│   │ 
│   ├── view/
│       └── all_views.json
│       └── full_{split}_views.json # split = testing, training, validation
│
└── pairs/
│   ├── 1_to_1_mapping_pairs.json
│   ├── 16_to_16_mapping_pairs.json
│   ├── 32_to_32_mapping_pairs.json
│   ├── olatverse.json
│   └── ...
│
└── rendered/
    ├── training/
    │   └── subset_{subset_uid}/
    │       └── {object_uid_0}.tar.gz
    │       │    ├── info.json
    │       │    ├── V18-Indoor_9C4A5417-f5d395fb63_6_image.png
    │       │    ├── V18-Outdoor_9C4A0573-f5d395fb63_6_image.png
    │       │    ├── V18_depth_0001.exr
    │       │    ├── V23-Indoor_9C4A5417-f5d395fb63_6_image.png
    │       │    └── ...
    │       └── ...
    │
    ├── testing/
    │   ├── {object_uid_1}.tar.gz
    │   ├── {object_uid_2}.tar.gz
    │   └── ...
    │   
    └── validation/
        ├── {object_uid_3}.tar.gz
        ├── {object_uid_4}.tar.gz
        └── ...
```

### Inside Each Object Folder / Unzipped Archive:

- **`info.json`**: Metadata containing object information, rendering parameters, camera poses, and lighting conditions.
- **`Vxxx-yyy_image.png`**: Rendered RGB images where:
  - `Vxxx` indicates the camera pose/viewpoint (e.g., `V18`, `V23`).
  - `yyy` is the lighting map name (e.g., `Indoor_9C4A5417-f5d395fb63_6` or `Outdoor_9C4A0573-f5d395fb63_6`).
- **`Vxxx_depth_*.exr`**: Corresponding ground-truth depth maps in OpenEXR format for each viewpoint.
### File Naming Convention Example:
```text
V18-Indoor_9C4A5417-f5d395fb63_6_image.png
│  │       │                   │
│  │       │                   └─ Rotating number (0,1,...,15)
│  │       │
│  │       └─ Lighting map identifier(9C4A5417-f5d395fb63, etc.)
│  └─ Lighting type (Indoor/Outdoor)
└─ Viewpoint ID (V18, V23, etc.)
```
## 📚 References
If you use this dataset in your research, please cite our work along with the prior work we built upon:
```bibtex
@misc{wang2026relightformerfeedforwardgenerativetransformer,
      title={RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting}, 
      author={Hejun Wang and Jinxi Li and Junwei Jiang and Shiwei Mao and Hu Cheng and Shouwang Huang and Bo Yang},
      year={2026},
      eprint={2609.07414},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2609.07414}, 
}
@article{Jin2024,
    author = {Jin, Haian and Li, Yuan and Luan, Fujun and Xiangli, Yuanbo and Bi, Sai and Zhang, Kai and Xu, Zexiang and Sun, Jin and Snavely, Noah},
    journal = {NeurIPS},
    title = {{Neural Gaffer: Relighting Any Object via Diffusion}},
    year = {2024}
}
@article{laval_indoor,
    author = {Marc-Andr\'{e} Gardner and Kalyan Sunkavalli and Ersin Yumer and Xiaohui Shen and Emiliano Gambaretto and Christian Gagn\'{e} and Jean-Fran\c{c}ois Lalonde},
    title = {Learning to Predict Indoor Illumination from a Single Image},
    journal = {SIGGRAPH Asia},
    year = {2017}
}
@INPROCEEDINGS{laval_outdoor, 
    author={Hold-Geoffroy, Yannick and Athawale, Akshaya and Lalonde, Jean-François}, 
    booktitle={CVPR},  
    title={Deep Sky Modeling for Single Image Outdoor Lighting Estimation},  
    year={2019}
} 
```
## 📮 Contact
For questions about the dataset, please contact:
- {hejun.wang, jinxi.li}@connect.polyu.hk
- bo.yang@polyu.edu.hk
---
*For rendering code, models, and further details, please visit our [GitHub Repository](https://github.com/vLAR-group/RelightFormer).*

	---
	language:
	- en
	tags:
	- relighting
	- multi-view
	- 3d
	- objaverse
	- hdr
	- rendering
	- depth-maps
	task_categories:
	- image-to-image
	- depth-estimation
	license: cc-by-4.0
	size_categories:
	- 10M<n<100M
	---

	<p align="center">
	<svg width="100%" height="100" viewBox="0 0 1000 100" xmlns="http://www.w3.org/2000/svg">
	<defs>
	<!-- 定义蓝-紫-粉的科技风渐变 -->
	<linearGradient id="gradientText" x1="0%" y1="0%" x2="100%" y2="0%">
	<stop offset="0%" style="stop-color:#00c6ff;stop-opacity:1" />
	<stop offset="50%" style="stop-color:#9d50bb;stop-opacity:1" />
	<stop offset="100%" style="stop-color:#ff00cc;stop-opacity:1" />
	</linearGradient>
	</defs>
	<text x="50%" y="55%" font-family="Arial, sans-serif" font-size="75" font-weight="900" fill="url(#gradientText)" text-anchor="middle" dominant-baseline="middle" letter-spacing="2">Laval Objaverse Dataset</text>
	</svg>
	</p>

	<p align="center">
	<img src="https://raw.githubusercontent.com/vLAR-group/RelightFormer/main/demo/teaser.jpg" alt="Teaser" width="80%">
	</p>

	<p align="center">
	<strong>vLAR Group</strong> \| <em>SIGGRAPH Asia 2026</em>
	</p>

	<p align="center">
	<a href="https://github.com/vLAR-group/RelightFormer">
	<img src="https://img.shields.io/badge/GitHub-Repository-181717?logo=github" alt="GitHub">
	</a>
	<a href="https://arxiv.org/abs/2609.07414"><img src="https://img.shields.io/badge/arXiv-2604.07414-b31b1b.svg" alt="arXiv"></a>
	<a href="https://huggingface.co/datasets/vLAR/LavalObjaverseDataset"><img src="https://img.shields.io/badge/🤗-Dataset-yellow" alt="Dataset"></a>
	<a href="https://huggingface.co/vLAR/RelightFormer"><img src="https://img.shields.io/badge/🤗-Model-yellow" alt="Model"></a>
	<a href="#license"><img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg" alt="License"></a>
	</p>

	<p align="center">
	<strong>A large-scale, high-quality dataset for multi-view relighting.</strong>
	</p>

	---

	## 📖 Dataset Summary

	The Laval Objaverse Dataset is a comprehensive dataset designed for multi-view relighting and novel view synthesis tasks. It combines high-quality 3D assets from Objaverse with realistic, diverse illumination conditions from the Laval Indoor and Outdoor HDR datasets. Each render includes synchronized multi-view images, depth maps, and complete lighting metadata.

	## 🏗️ Dataset Construction

	We structured our dataset through a rigorous four-step pipeline:

	1. 🎯 Object Filtering:
	We source base meshes from the [Objaverse](https://objaverse.allenai.org/) dataset. To ensure high visual fidelity, we exclude meshes with poor geometry or materials by adopting the strict object selection criteria from the relitObjaverse dataset [[1]](#references).

	2. 💡 Lighting Selection:
	We employ open-source illumination datasets, specifically the Laval Indoor HDR Dataset [[2]](#references) and the Laval Outdoor HDR Dataset [[3]](#references). Each illumination map is uniformly rotated 16 times to maximize lighting diversity. For every object, we sample 16 environment maps with a balanced 1:1 ratio of indoor and outdoor lighting conditions.

	3. 📷 Rendering Protocol:
	All objects are normalized to unit size (largest bounding box dimension = 1) and placed at the origin. Cameras are uniformly distributed on a sphere at distances of 1.8 or 2.2 units from the center, oriented toward the scene center.

	4. 🛡️ Dataset Partitioning:
	The dataset is split into training, validation, and test sets with strict disjointness: no camera poses, illumination conditions, or 3D objects overlap between splits. The division information are under `info/laval`, `info/objaverse` and `info/view` for illuminations, objects and viewpoints respecitively.

	## 📂 Dataset Structure

	```text
	LavalObjaverseDataset/
	└── info/
	│ ├── laval/
	│ │ └── full_testing_lighting.json
	│ │ └── full_training_lighting.json
	│ │ └── full_validation_lighting.json
	│ │
	│ ├── objaverse/
	│ │ └── all_objects.json
	│ │ └── full_{split}_objects.json # split = testing, training, validation
	│ │
	│ ├── view/
	│ └── all_views.json
	│ └── full_{split}_views.json # split = testing, training, validation
	│
	└── pairs/
	│ ├── 1_to_1_mapping_pairs.json
	│ ├── 16_to_16_mapping_pairs.json
	│ ├── 32_to_32_mapping_pairs.json
	│ ├── olatverse.json
	│ └── ...
	│
	└── rendered/
	├── training/
	│ └── subset_{subset_uid}/
	│ └── {object_uid_0}.tar.gz
	│ │ ├── info.json
	│ │ ├── V18-Indoor_9C4A5417-f5d395fb63_6_image.png
	│ │ ├── V18-Outdoor_9C4A0573-f5d395fb63_6_image.png
	│ │ ├── V18_depth_0001.exr
	│ │ ├── V23-Indoor_9C4A5417-f5d395fb63_6_image.png
	│ │ └── ...
	│ └── ...
	│
	├── testing/
	│ ├── {object_uid_1}.tar.gz
	│ ├── {object_uid_2}.tar.gz
	│ └── ...
	│
	└── validation/
	├── {object_uid_3}.tar.gz
	├── {object_uid_4}.tar.gz
	└── ...
	```

	### Inside Each Object Folder / Unzipped Archive:

	- `info.json`: Metadata containing object information, rendering parameters, camera poses, and lighting conditions.
	- `Vxxx-yyy_image.png`: Rendered RGB images where:
	- `Vxxx` indicates the camera pose/viewpoint (e.g., `V18`, `V23`).
	- `yyy` is the lighting map name (e.g., `Indoor_9C4A5417-f5d395fb63_6` or `Outdoor_9C4A0573-f5d395fb63_6`).
	- *`Vxxx_depth_.exr`**: Corresponding ground-truth depth maps in OpenEXR format for each viewpoint.

	### File Naming Convention Example:
	```text
	V18-Indoor_9C4A5417-f5d395fb63_6_image.png
	│ │ │ │
	│ │ │ └─ Rotating number (0,1,...,15)
	│ │ │
	│ │ └─ Lighting map identifier(9C4A5417-f5d395fb63, etc.)
	│ └─ Lighting type (Indoor/Outdoor)
	└─ Viewpoint ID (V18, V23, etc.)
	```

	## 📚 References

	If you use this dataset in your research, please cite our work along with the prior work we built upon:
	```bibtex
	@misc{wang2026relightformerfeedforwardgenerativetransformer,
	title={RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting},
	author={Hejun Wang and Jinxi Li and Junwei Jiang and Shiwei Mao and Hu Cheng and Shouwang Huang and Bo Yang},
	year={2026},
	eprint={2609.07414},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2609.07414},
	}

	@article{Jin2024,
	author = {Jin, Haian and Li, Yuan and Luan, Fujun and Xiangli, Yuanbo and Bi, Sai and Zhang, Kai and Xu, Zexiang and Sun, Jin and Snavely, Noah},
	journal = {NeurIPS},
	title = {{Neural Gaffer: Relighting Any Object via Diffusion}},
	year = {2024}
	}

	@article{laval_indoor,
	author = {Marc-Andr\'{e} Gardner and Kalyan Sunkavalli and Ersin Yumer and Xiaohui Shen and Emiliano Gambaretto and Christian Gagn\'{e} and Jean-Fran\c{c}ois Lalonde},
	title = {Learning to Predict Indoor Illumination from a Single Image},
	journal = {SIGGRAPH Asia},
	year = {2017}
	}

	@INPROCEEDINGS{laval_outdoor,
	author={Hold-Geoffroy, Yannick and Athawale, Akshaya and Lalonde, Jean-François},
	booktitle={CVPR},
	title={Deep Sky Modeling for Single Image Outdoor Lighting Estimation},
	year={2019}
	}
	```

	## 📮 Contact

	For questions about the dataset, please contact:

	- {hejun.wang, jinxi.li}@connect.polyu.hk
	- bo.yang@polyu.edu.hk

	---

	For rendering code, models, and further details, please visit our [GitHub Repository](https://github.com/vLAR-group/RelightFormer).