GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object with Gaussian Splatting

Project Page | Paper | Paper (Compressed) | Dataset

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.

refresh.mp4

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 $\mathcal{L}_{\text{gs}}$.

Setup

CUDA

GaussianObject is tested with CUDA 11.7. 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 requirements.txt.

Cloning the Repository

The repository contains submodules. Please clone it with

git clone [email protected]:GaussianObject/GaussianObject.git --recursive

or update submodules in GaussianObject directory with

git submodule update --init --recursive

Dataset

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 should be

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 SegAnyGAussians. 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 pred_monodepth.py -s <YOUR_DATA_DIR>

Python Environment

GaussianObject is tested with Python 3.10. All the required packages are listed in requirements.txt. You can install them with

# setup pip packages
pip install -r requirements.txt

# setup submodules
pip install -e submodules/diff-gaussian-rasterization
pip install -e submodules/simple-knn
pip install -e submodules/pytorch3d
pip install -e submodules/minLoRA
pip install -e submodules/CLIP

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

python download_hf_models.py

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

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

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

# 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

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

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

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

# 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.

Acknowledgement

Some code of GaussianObject is based on 3DGS, threestudio and ControlNet. Thanks for their great work!