metadata

license: mit

G-buffer Objaverse

G-buffer Objaverse: High-Quality Rendering Dataset of Objaverse.

Chao Xu, Yuan Dong, Qi Zuo, Junfei Zhang, Xiaodan Ye, Wenbo Geng, Yuxiang Zhang, Xiaodong Gu, Lingteng Qiu, Zhengyi Zhao, Qing Ran, Jiayi Jiang, Zilong Dong, Liefeng Bo

TODO

Release objaverse-xl alignment rendering data

News

We have released a compressed version of the datasets, check the downloading tips! (01.14, 2024 UTC)
Thanks for JunzheJosephZhu for improving the robustness of the downloading scripts. Now you could restart the download script from the break point. (01.12, 2024 UTC)
Release 10 Category Annotation of the Objaverse Subset (01.06, 2024 UTC)
Release G-buffer Objaverse Rendering Dataset (01.06, 2024 UTC)

Download

Download gobjaverse (6.5T) rendering dataset using following scripts.

# download_gobjaverse_280k index file
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/gobjaverse_280k.json
# Example: python ./scripts/data/download_gobjaverse_280k.py ./gobjaverse_280k ./gobjaverse_280k.json 10
python ./download_gobjaverse_280k.py /path/to/savedata /path/to/gobjaverse_280k.json nthreads(eg. 10)
# Or if the network is not so good, we have provided a compressed verison with each object as a tar file
# To download the compressed version(only 260k tar files)
python ./download_objaverse_280k_tar.py /path/to/savedata /path/to/gobjaverse_280k.json nthreads(eg. 10)
# download gobjaverse_280k/gobjaverse index to objaverse
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/gobjaverse_280k_index_to_objaverse.json
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/gobjaverse_index_to_objaverse.json
# download Cap3D text-caption file
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/text_captions_cap3d.json

The 10 general categories including Human-Shape (41,557), Animals (28,882), Daily-Used (220,222), Furnitures (19,284), Buildings&&Outdoor (116,545), Transportations (20,075), Plants (7,195), Food (5,314), Electronics (13,252) and Poor-quality (107,001).
Download the category annotation using following scripts.

# download category annotation
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/category_annotation.json
# If you want to download a specific category in gobjaverse280k:
# Step1: download the index file of the specified category.
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/gobjaverse_280k_split/gobjaverse_280k_{category_name}.json # category_name: Human-Shape, ...
# Step2: download using script.
# Example: python ./scripts/data/download_gobjaverse_280k.py ./gobjaverse_280k_Human-Shape ./gobjaverse_280k_Human-Shape.json 10
python ./download_gobjaverse_280k.py /path/to/savedata /path/to/gobjaverse_280k_{category_name}.json nthreads(eg. 10)

Folder Structure

The structure of gobjaverse rendering dataset:

|-- ROOT
    |-- dictionary_id
        |-- instance_id
            |-- campos_512_v4
                |-- 00000
                    |-- 00000.json  # Camera Information
                    |-- 00000.png   # RGB 
                    |-- 00000_albedo.png  # Albedo 
                    |-- 00000_hdr.exr  # HDR
                    |-- 00000_mr.png  # Metalness and Roughness
                    |-- 00000_nd.exr  # Normal and Depth
                |-- ...

Coordinate System

Normal Coordinate System

The 3D coordinate system definition is very complex. it is difficult for us to say what the camera system used. Fortunately, the target we want to get is mapping the world normal of rendering system to Normal-Bae system, as the following figure illustrates:

where the U-axis and V-axis denote the width-axis and height-axis in image space, respectively, the xyz is the Normal-Bae camera view coordinate system.

Note that public rendering system for Objaverse is blender-based system:

However, our rendering system is defined at Unity-based system, seeing:

A question is how do we plug in blender's coordinate system directly without introducing a new coordinate system?

A possible solution is that we maintain world to camera transfer matrix as blender setting, transferring Unity-based system to blender-based system

We provide example codes to visualize the coordinate mapping.

# example of coordinate experiments
## download datasets
wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/Lingtengqiu/render_data_examples.zip
unzip render_data_examples.zip

## visualizing blender-based system, and warping world-space normal to normal-bae system.
python ./process_blender_dataset.py

## visualizing our system, and warping world-space normal to normal-bae system.
python ./process_unity_dataset.py

Depth-Warpping

We write an example to demonstrate that how to obtain intrinsic matrix K, and warp ref image to target image based on ref depth map.

# build quick-zbuff code
mkdir -p ./lib/build
g++ -shared -fpic -o ./lib/build/zbuff.so ./lib/zbuff.cpp

# an demo for depth-based Warpping 
# python ./depth_warp_example.py $REFVIEW $TARGETVIEW
python3 ./depth_warp_example.py 0 3

Citation

@article{qiu2023richdreamer,
    title={RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D}, 
    author={Lingteng Qiu and Guanying Chen and Xiaodong Gu and Qi zuo and Mutian Xu and Yushuang Wu and Weihao Yuan and Zilong Dong and Liefeng Bo and Xiaoguang Han},
    year={2023},
    journal = {arXiv preprint arXiv:2311.16918}
}

@article{objaverse,
    title={Objaverse: A Universe of Annotated 3D Objects},
    author={Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs and
            Oscar Michel and Eli VanderBilt and Ludwig Schmidt and
            Kiana Ehsani and Aniruddha Kembhavi and Ali Farhadi},
    journal={arXiv preprint arXiv:2212.08051},
    year={2022}
}

Datasets:

3DAIGC
/

gobjaverse

G-buffer Objaverse

Project page

Github

YouTube

RichDreamer

ND-Diffusion Model