metadata
license: mit
task_categories:
- text-to-3d
- image-to-3d
- text-to-image
- image-to-image
language:
- en
tags:
- objaverse
- 3d
- high-quality
Objaverse-Ortho10View
Github | Project Page | Paper
1. Dataset Introduction
TL;DR: This dataset contains multi-view images that are rendered from a high-quality subset of Objaverse, used in MV-Adapter. Features:
- Orthographic views
- 1024x1024 resolution
- RGB, Depth, Normal, Camera
2. Data Extraction
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/datasets/huanngzh/Objaverse-Ortho10View
cat Objaverse-Render.part* > Objaverse-Ortho10View.tar.gz
tar -xzvf Objaverse-Ortho10View.tar.gz
3. File Structure
Objaverse-Ortho10View
├── data
│ ├── 00
│ │ ├── 00a4d2b0c4c240289ed456e87d8b9e02
│ │ │ ├── color_0000.webp # rgb
│ │ │ ├── color_0001.webp
│ │ │ ├── ...
│ │ │ ├── color_0009.webp
│ │ │ ├── depth_0000.exr # depth
│ │ │ ├── depth_0001.exr
│ │ │ ├── ...
│ │ │ ├── depth_0009.exr
│ │ │ ├── normal_0000.webp # normal
│ │ │ ├── normal_0001.webp
│ │ │ ├── ...
│ │ │ ├── normal_0009.webp
│ │ │ └── meta.json # camera
│ │ ...
│ │ └── 00a6e74d8eeb428bb3e01b9361d8d57e
│ ├── 0a
│ ├── ...
│ └── zS
├── objaverse_list_6w.json # id list
└── objaverse_short_captions.json # captions
| 0000 (elevation=0, azimuth=0) | 0001 (elevation=0, azimuth=90) | 0002 (elevation=0, azimuth=180) | 0003 (elevation=0, azimuth=270) | 0004 (elevation=0, azimuth=45) |
|---|---|---|---|---|
 |
 |
 |
 |
 |
| 0005 (elevation=0, azimuth=315) | 0006 (elevation=89.99, azimuth=0) | 0007 (elevation=-89.99, azimuth=0) | 0008 (elevation=89.99, azimuth=180) | 0009 (elevation=-89.99, azimuth=180) |
 |
 |
 |
 |
 |
Citation
If you find this dataset useful, please cite:
@article{huang2024mvadapter,
title={Mv-adapter: Multi-view consistent image generation made easy},
author={Huang, Zehuan and Guo, Yuan-Chen and Wang, Haoran and Yi, Ran and Ma, Lizhuang and Cao, Yan-Pei and Sheng, Lu},
journal={arXiv preprint arXiv:2412.03632},
year={2024}
}
@inproceedings{deitke2023objaverse,
title={Objaverse: A universe of annotated 3d objects},
author={Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and Kembhavi, Aniruddha and Farhadi, Ali},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={13142--13153},
year={2023}
}