GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting
SIGGRAPH Asia 2024 (ACM Transactions on Graphics)
Project Page | Paper | Video | 
GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting
Chen Yang1, Sikuang Li1, Jiemin Fang2†, Ruofan Liang3, Lingxi Xie2, Xiaopeng Zhang2, Wei Shen1✉, Qi Tian2
1MoE Key Lab of Artificial Intelligence, AI Institute, SJTU 2Huawei Inc. 3University of Toronto
*Equal contribution. †Project lead. ✉Corresponding author.
🚩 News
- 🤖 We provide a step-by-step guideline for COLMAP-free GaussianObject. Now you can use GaussianObject to reconstruct arbitary captured objects!
- 🔥 GaussianObject has been accepted by ACM TOG (SIGGRAPH Asia 2024)! See you in Tokyo!
https://github.com/user-attachments/assets/a388150a-2f90-4ced-ad90-d4aac48c39dc
We propose GaussianObject, a framework to represent and render the 3D object with Gaussian splatting, that achieves high rendering quality with only 4 input images even under COLMAP-free conditions.
We first introduce techniques of visual hull and floater elimination which explicitly inject structure priors into the initial optimization process for helping build multi-view consistency, yielding a coarse 3D Gaussian representation. Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined. We design a self-generating strategy to obtain image pairs for training the repair model. Our GaussianObject achives strong reconstruction results from only 4 views and significantly outperforms previous state-of-the-art methods.
- We initialize 3D Gaussians by constructing a visual hull with camera parameters and masked images, optimizing them with the $\mathcal{L}_{\text{gs}}$ and refining through floater elimination.
- We use a novel `leave-one-out' strategy and add 3D noise to Gaussians to generate corrupted Gaussian renderings. These renderings, paired with their corresponding reference images, facilitate the training of the Gaussian repair model employing $\mathcal{L}_{\text{tune}}$.
- Once trained, the Gaussian repair model is frozen and used to correct views that need to be rectified. These views are identified through distance-aware sampling. The repaired images and reference images are used to further optimize 3D Gaussians with $
\mathcal{L}_{\text{rep}}$ and $\mathcal{L}_{\text{gs}}$.
⚡ Colab
Sang Han provides a Colab script for GaussianObject in #9. Thanks for the contribution of the community! If you are experiencing issues with insufficient GPU VRAM, try this.
🚀 Setup
CUDA
GaussianObject is tested with CUDA 11.8. If you are using a different version, you can choose to install nvidia/cuda in a local conda environment or modify the version of PyTorch in section Python Environment.
Cloning the Repository
The repository contains submodules. Please clone it with
git clone https://github.com/GaussianObject/GaussianObject.git --recursiveor update submodules in GaussianObject directory with
git submodule update --init --recursiveDataset
You can try GaussianObject with the Mip-NeRF360 dataset and OmniObject3D dataset. The data can be downloaded in Google Drive.
The directory structure of the dataset is as follows:
```text GaussianObject ├── data │ ├── mip360 │ │ ├── bonsai │ │ │ ├── images │ │ │ ├── images_2 │ │ │ ├── images_4 │ │ │ ├── images_8 │ │ │ ├── masks │ │ │ ├── sparse │ │ │ ├── zoe_depth │ │ │ ├── zoe_depth_colored │ │ │ ├── sparse_4.txt │ │ │ ├── sparse_6.txt │ │ │ ├── sparse_9.txt │ │ │ └── sparse_test.txt │ │ ├── garden │ │ └── kitchen │ └── omni3d └── ... ``` `images`, `images_2`, `images_4`, `images_8` and `sparse` are from the original dataset. `masks` is the object mask generated with [segment-anything](https://github.com/facebookresearch/segment-anything). `zoe_depth` and `zoe_depth_colored` are the depth maps and colored depth maps. `sparse_4.txt`, `sparse_6.txt` and `sparse_9.txt` are train set image ids and `sparse_test.txt` is the test set.To test GaussianObject with your own dataset, you can manually prepare the dataset with the same directory structure. The depth maps and colored depth maps are generated with
python preprocess/pred_monodepth.py -s <YOUR_DATA_DIR>Python Environment
GaussianObject is tested with Python 3.11. All the required packages are listed in requirements.txt. You can install them with
# install pytorch
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
# setup pip packages
pip install -r requirements.txt
# (Optional) setup croco for DUSt3R
cd submodules/croco/models/curope/
python setup.py build_ext --inplace
cd ../../../..Pretrained ControlNet Model
Pretrained weights of Stable Diffusion v1.5 and ControlNet Tile need to be put in models/ following the instruction of ControlNet 1.1 with our given script:
cd models
python download_hf_models.py
cd ..💪 Run the Code
Taking the scene kitchen from mip360 dataset as an example, GaussianObject generate the visual hull of it, train a coarse 3DGS representation, analyze the statistical regularity of the coarse model with leave-one-out strategy, fine-tune the Gaussian Repair Model with LoRA and repair the 3DGS representation step by step.
Visual Hull
Train script:
```sh python visual_hull.py \ --sparse_id 4 \ --data_dir data/mip360/kitchen \ --reso 2 --not_vis ``` The visual hull is saved in `data/mip360/kitchen/visual_hull_4.ply`.Coarse 3DGS
Train script:
```sh python train_gs.py -s data/mip360/kitchen \ -m output/gs_init/kitchen \ -r 4 --sparse_view_num 4 --sh_degree 2 \ --init_pcd_name visual_hull_4 \ --white_background --random_background ``` You can render the coarse model it with ```sh # render the test set python render.py \ -m output/gs_init/kitchen \ --sparse_view_num 4 --sh_degree 2 \ --init_pcd_name visual_hull_4 \ --white_background --skip_all --skip_train # render the path python render.py \ -m output/gs_init/kitchen \ --sparse_view_num 4 --sh_degree 2 \ --init_pcd_name visual_hull_4 \ --white_background --render_path ``` The rendering results are saved in `output/gs_init/kitchen/test/ours_10000` and `output/gs_init/kitchen/render/ours_10000`.Leave One Out
Train script:
```sh python leave_one_out_stage1.py -s data/mip360/kitchen \ -m output/gs_init/kitchen_loo \ -r 4 --sparse_view_num 4 --sh_degree 2 \ --init_pcd_name visual_hull_4 \ --white_background --random_background python leave_one_out_stage2.py -s data/mip360/kitchen \ -m output/gs_init/kitchen_loo \ -r 4 --sparse_view_num 4 --sh_degree 2 \ --init_pcd_name visual_hull_4 \ --white_background --random_background ```LoRA Fine-Tuning
Train script:
```sh python train_lora.py --exp_name controlnet_finetune/kitchen \ --prompt xxy5syt00 --sh_degree 2 --resolution 4 --sparse_num 4 \ --data_dir data/mip360/kitchen \ --gs_dir output/gs_init/kitchen \ --loo_dir output/gs_init/kitchen_loo \ --bg_white --sd_locked --train_lora --use_prompt_list \ --add_diffusion_lora --add_control_lora --add_clip_lora ```Gaussian Repair
Train script:
```sh python train_repair.py \ --config configs/gaussian-object.yaml \ --train --gpu 0 \ tag="kitchen" \ system.init_dreamer="output/gs_init/kitchen" \ system.exp_name="output/controlnet_finetune/kitchen" \ system.refresh_size=8 \ data.data_dir="data/mip360/kitchen" \ data.resolution=4 \ data.sparse_num=4 \ data.prompt="a photo of a xxy5syt00" \ data.refresh_size=8 \ system.sh_degree=2 ``` The final 3DGS representation is saved in `output/gaussian_object/kitchen/save/last.ply`. You can render it with ```sh # render the test set python render.py \ -m output/gs_init/kitchen \ --sparse_view_num 4 --sh_degree 2 \ --init_pcd_name visual_hull_4 \ --white_background --skip_all --skip_train \ --load_ply output/gaussian_object/kitchen/save/last.ply # render the path python render.py \ -m output/gs_init/kitchen \ --sparse_view_num 4 --sh_degree 2 \ --init_pcd_name visual_hull_4 \ --white_background --render_path \ --load_ply output/gaussian_object/kitchen/save/last.ply ``` The rendering results are saved in `output/gs_init/kitchen/test/ours_None` and `output/gs_init/kitchen/render/ours_None`.📸 Try Your Casually Captured Data
GaussianObject can work without accurate camera poses (usually from COLMAP) and masks, which we term it as CF-GaussianObject.
Here is the guideline for CF-GaussianObject:
To use CF-GaussianObject (COLMAP-free GaussianObject), you need to download [SAM](https://github.com/facebookresearch/segment-anything) and [DUSt3R](https://github.com/naver/dust3r) or [MASt3R](https://github.com/naver/mast3r) checkpoints. ```sh cd models sh download_preprocess_models.sh cd .. ``` Assume you have a dataset with 4 images, it should be put in `./data` as the following structure ```text GaussianObject ├── data │ ├──🌏 Citation
If you find GaussianObject useful for your work please cite:
@article{yang2024gaussianobject,
title = {GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting},
author = {Chen Yang and Sikuang Li and Jiemin Fang and Ruofan Liang and
Lingxi Xie and Xiaopeng Zhang and Wei Shen and Qi Tian},
journal = {ACM Transactions on Graphics},
year = {2024}
}🤗 Acknowledgement
Some code of GaussianObject is based on 3DGS, threestudio and ControlNet. Thanks for their great work!