Awesome

News

• [2024.4.18] 🔥 Release paper, inference code, and pretrained checkpoint.
• [On-going] Clean and organize the masks corresponding to the dataset used in the experiments.
• [On-going] Scale up the model with more training data and release stronger models as the foundation model for downstream tasks.
• [To-do] Release training code.

Installation

Install with conda:

conda env create -f environment.yaml
conda activate infusion

• 🛠️ For rendering depth, we use the diff-gaussian-rasterization-confidence from FSGS, thanks to their work! :)

Download Checkpoints

Download the Infusion checkpoint and put it in the 'checkpoints' folder:

• HuggingFace

Data Preparation

Our experiments are conducted on the datasets provided by Mip-NeRF, Instruct-NeRF2NeRF, and SPIn-NeRF. We will upload the masks used in the experiments and the challenge scene we shot ourselves in a few days.

Taking "garden" in Mip-NeRF as an example, each scene folder should be organized as follows.

garden
├── images # RGB data
│   ├── DSC07956.JPG
│   ├── DSC07957.JPG
│   └── ...                   
├── seg # Mask 
│   ├── DSC07956.JPG
│   ├── DSC07957.JPG
│   └── ... 
│   # The part that needs to be inpainted is white
└── sparse # Colmap
│   └── 0
│       └── ...

• 🛠️ You can prepare your own data according to such a structure. In addition, accurate masks are very important. Here, we recommend two image segmentation tools: Segment and Track Anything and Grounded SAM.

• 🛠️ To obtain camera parameters and initial point cloud, please refer to 'convert.py' in Gaussian-Splatting :)

Instructions

The entire pipeline is divided into three stages:

• Train the Incomplete Gaussians.
• Inpaint Gaussians via Diffusion Prior.
• Combine Inpainted Gaussians and Incomplete Gaussians.

🌺 Stage 1

Use pre-annotated masks to train incomplete Gaussians.

cd gaussian_splatting
# Train incomplete Gaussians
python train.py -s <path to scene folder> -m <path to output folder> -u nothing --mask_training
#--color_aug

# Obtain c2w matrix, intrinsic matrix, incomplete depth, and rgb rendering image
python render.py -s <path to scene folder> -m <path to output folder> -u nothing

• 🛠️ Tip: Sometimes, the rendered depth has too many empty points. Maybe you can use --color_aug during training, which will randomly select the background color when rendering depth, which may make the depth map more reliable.

• 🛠️ Recently, some works focused on how to segment Gaussians. This is not the focus of this work, so a relatively simple method was chosen. :)

🌺 Stage 2

Inpaint Gaussians using depth inpainting model.

• 📢 You need to select a single image and in 'path to output folder/train/ours_30000/renders' and mark the area that needs to be inpainted and save it as 'mask.png'. (It doesn’t have to be precise but it needs to cover all the missing parts.)

• 📢 Next, you need to inpaint a single image. Here are some great tools to inpaint a single image: Stable Diffusion XL Inpainting and Photoroom. Here is an example:

  <table align="center"> <tr> <td> <img src="assets/Figures/inpaint_example.png"> </td> </tr> </table>

# Assume that the selected single image is named "DSC07956.JPG".
cd depth_inpainting/run
input_rgb_path=<path to inpainted single image>
input_mask_path=<path to 'mask.png'>
input_depth_path=<path to output folder/train/ours_30000/depth_dis/DSC07956.npy>
c2w=<path to output folder/train/ours_30000/c2w/DSC07956.npy>
intri=<path to output folder/train/ours_30000/intri/DSC07956.npy>
model_path=</path to depth_inpainting model checkpoint>  # absolute path
output_dir=<path to output folder>


CUDA_VISIBLE_DEVICES=0 python run_inference_inpainting.py \
            --input_rgb_path $input_rgb_path \
            --input_mask $input_mask_path \
            --input_depth_path $input_depth_path \
            --model_path $model_path \
            --output_dir $output_dir \
            --denoise_steps 20 \
            --intri $intri \
            --c2w $c2w \
            --use_mask\
            --blend  # Whether to use 'Blended Diffusion (https://arxiv.org/abs/2111.14818)' during inference. 

• 🛠️ Tip: If you feel that the depth map obtained by one inference is not satisfactory, you can use the newly obtained output_dir/<inpainted_image_name>_depth_dis.npy as the new $input_depth_path and loop two or three times to get better results.

🌺 Stage 3

Combine inpainted Gaussians and incomplete Gaussians and quickly fine-tune on inpainted single image.

# Assume that the selected single image is named "DSC07956.JPG".
origin_ply="path to output folder/point_cloud/iteration_30000/point_cloud.ply"
supp_ply="path to output folder/DSC07956_mask.ply"
save_ply="path to output folder/point_cloud/iteration_30001/point_cloud.ply"
# Combine inpainted Gaussians and incomplete Gaussians.
python compose.py --original_ply $origin_ply  --supp_ply $supp_ply --save_ply $save_ply --nb_points 100 --threshold 1.0
# Fine-tune on an inpainted single image for 150 iterations.
python train.py -s <path to scene folder> -m <path to output folder> -u DSC07956.JPG -n <path to inpainted single image> --load_iteration 30001 --iteration 150
# Render
python render.py -s <path to scene folder> -m <path to output folder> -u nothing --iteration 150

• 🛠️ The two parameters --nb_points and --threshold are used to remove floaters. Increasing their values will remove more surrounding points. Removing floaters is very important for the final rendering results. Here, we need to find the most suitable parameters for removing floaters from the scene.

• 🛠️ As explicit points, Gaussian can be directly edited and cropped in actual applications, such as KIRI Engine

Acknowledgements

This project is developed on the codebase of Gaussian-Splatting, Marigold and Magicboomliu. We appreciate their great works!

Citation

If you find this repository useful in your work, consider citing the following papers and giving a ⭐ to the public repository to allow more people to discover this repo:

@article{liu2024infusion,
  title={InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior},
  author={Liu, Zhiheng and Ouyang, Hao and Wang, Qiuyu and Cheng, Ka Leong and Xiao, Jie and Zhu, Kai and Xue, Nan and Liu, Yu and Shen, Yujun and Cao, Yang},
  journal={arXiv preprint arXiv:2404.11613},
  year={2024}
}