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README.md

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 ---
+language:
+- en
+tags:
+- relighting
+- multi-view
+- 3d
+- objaverse
+- hdr
+- rendering
+- depth-maps
+task_categories:
+- image-to-image
+- depth-estimation
+- image-generation
 license: cc-by-4.0
+size_categories:
+- 100K<n<1M
 ---
+
+<h1 align="center">Laval Objaverse Dataset</h1>
+
+<p align="center">
+  <strong>vLAR Group</strong> | <em>SIGGRAPH 2026</em>
+</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 2026</em>
+</p>
+
+<p align="center">
+  <a href="https://github.com/vLAR-group/RelightFormer">
+    <img src="https://img.shields.io/badge/Code-GitHub-black" alt="Code">
+  </a>
+  <a href="https://huggingface.co/datasets/vLAR/LavalObjaverseDataset">
+    <img src="https://img.shields.io/badge/Dataset-Hugging%20Face-yellow" alt="Dataset">
+  </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.
+
+📊 Key Statistics
+Total Environment Maps: 39,008 unique maps (indoor + outdoor)
+Lighting Variants: 16 rotations per environment map
+Views per Object: Multiple viewpoints (V18, V23, etc.) at 1.8 and 2.2 unit distances
+Split Ratio: Training/Validation/Test with strict disjointness
+Output Formats: PNG (RGB), EXR (Depth)
+
+## 🏗️ 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 **`laval/info`** and **`objaverse/info`**, for lighting map and objects respecitively.
+
+## 📂 Dataset Structure
+
+```text
+LavalObjaverseDataset/
+└── rendered/
+    ├── training/
+    │   └── subset_{subset_uid}/
+    │       └── {object_uid_0}.zip
+    │           ├── 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
+    │           └── V23_depth_0001.exr
+    ├── testing/
+    │   ├── {object_uid_1}.zip
+    │   ├── {object_uid_2}.zip
+    │   └── ...
+    └── validation/
+        ├── {object_uid_3}.zip
+        ├── {object_uid_4}.zip
+        └── ...
+```
+
+### 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
+│  └─ 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
+@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}
+} 
+```
+
+---
+*For rendering code, models, and further details, please visit our [GitHub Repository](https://github.com/vLAR-group/RelightFormer).*