mjones00 / snp

Official code for View Synthesis with Sculpted Neural Points

View Synthesis with Sculpted Neural Points

Official code for View Synthesis with Sculpted Neural Points

Setup

We recommend installing the dependencies using Anaconda. Run the following commands to create a new env:

conda create -n snp python=3.8
conda activate snp
conda install pytorch=1.9.0 torchvision=0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia
conda install -c fvcore -c iopath -c conda-forge fvcore iopath
conda install pytorch3d -c pytorch3d
conda install -c conda-forge plyfile imageio imageio-ffmpeg matplotlib tensorboard ffmpeg=4.3
pip install opencv-python==4.5.3.56 lpips==0.1.4 moviepy

Note: you will need a NVIDIA GPU with at least 11 GB memory to run the pre-trained checkpoints on LLFF and 48GB memory (we use A6000) to fully reproduce our results in the paper.

Prepare the Datasets

You can find the pre-processed DTU and LLFF datasets in the Google Drive link.

TanksAndTemples (TNT) dataset is under this link.

For the NeRF-Synthetic(SYNMVS) dataset, download from the original release.

Put the raw data and depths under ./data.

The datasets should have the following structure:

├── data                                                                                                                                                                                                       
│   ├── DTU                                                                                                  
│   │   └── depths                                                                                                                            
│   │   └── raw                                                                            
|   ├── LLFF
|   |   └── depths                                                                                                                            
│   │   └── raw
|   ├── TanksAndTemples
|   |   └── depths  
|   |   └── processed_masks
|   |   └── ...
|   ├── nerf-synthetic
|   |   └── lego
|   |   └── ...

Evaluate the Pre-trained Models

The easiest way to get started is to try our pre-trained checkpoints.

You can find the saved checkpoints/pointclouds in the Google Drive link.

Put the downloaded *.pth and *.pt under ./saved_checkpoints and ./saved_pointclouds, respectively.

For the TNT dataset, the checkpoints and pointclouds are in this link and this link, respectively.

For the NeRF-Synthetic(SYNMVS) dataset, the checkpoints and pointclouds are in this link and this link, respectively.

The checkpoints' file size could be large. Therefore, you can download the checkpoints on each individual scenes separately, but please put them following this structure:

├── saved_checkpoints                                                                                                                                                                                                      
│   ├── DTU                                                                                                  
│   │   └── ..._scan1_....pth   # checkpoints for the scan1 scene 
|   |   └── ...                                                                        
|   ├── LLFF
|   |   └── ..._fern_....pth   # checkpoints for the fern scene 
|   |   └── ...  
|   ├── TNT
|   |   └── ..._Ignatius_....pth   # checkpoints for the Ignatius scene 
|   |   └── ...  
|   ├── SYNMVS
|   |   └── ..._lego_....pth   # checkpoints for the lego scene 
|   |   └── ...  
|
├── saved_pointclouds                                                                                                                                                                                                      
│   ├── DTU                                                                                                  
│   │   └── ..._scan1_....pt   # sculpted pointclouds for the scan1 scene 
|   |   └── ...                                                                        
|   ├── LLFF
|   |   └── ..._fern_....pt   # sculpted pointclouds for the fern scene 
|   |   └── ...  
|   ├── TNT
|   |   └── Ignatius.ply   # sculpted pointclouds for the Ignatius scene 
|   |   └── ...  
|   ├── SYNMVS
|   |   └── ..._lego_....ply   # sculpted pointclouds for the lego scene 
|   |   └── ...  

For DTU and LLFF, run the following command:

sh eval.sh <dataset_name> <scene_name>

Where <dataset_name> is DTUHR for the DTU dataset and LLFF for the LLFF dataset.

For TNT, run the following command:

sh TNT_Masked_pcloud_pulsar_eval.sh <scene_name>

For NeRF-Synthetic, run the following command:

sh SYNMVS_pcloud_pulsar_eval.sh <scene_name>

For example, if you run

sh eval.sh LLFF fern

you will find this GIF in ./saved_videos and the evaluation numbers are printed in the command line.

Train from Scratch

Run

sh train.sh <dataset_name> <scene_name>

to train the whole pipeline (including point sculpting and the final training). It takes about 14 hours on a single A6000 GPU.

You can find the tensorboard logs under ./tb folder. The final checkpoints and pointclouds will be stored under ./checkpoints and ./pointclouds, respectively.

Note: for people who don't have GPUs with 48GB memory and/or want higher rendering speed, there are a few hyperparameters that you can play with. From our experience, changing these values (in a reasonable range) has a minor effect on the rendered image quality.

• Reduce max_num_pts which controls the point cloud size.
• Adjust crop_h and crop_w to control the image resolution.

Run SNP on Your Own Scenes

See the link here.

Citation

If you find this repository useful, please cite

@article{zuo2022view,
  title={View Synthesis with Sculpted Neural Points},
  author={Yiming Zuo and Jia Deng},
  journal={arXiv preprint arXiv:2205.05869},
  year={2022}
}