Mercerai / PFGS

PFGS: High Fidelity Point Cloud Rendering via Feature Splatting

PFGS: High Fidelity Point Cloud Rendering via Feature Splatting
Jiaxu Wang^†, Ziyi Zhang^†, Junhao He, Renjing Xu*
ECCV 2024

If you found this project useful, please cite us in your paper, this is the greatest support for us.

Requirements (Tested on 1 * RTX3090)

• Linux
• Python == 3.8
• Pytorch == 1.13.0
• CUDA == 11.7

Installation

Install from environment.yml

You can directly install the requirements through:

$ conda env create -f environment.yml

Or install packages seperately

• Create Environment

  $ conda create --name PFGS python=3.8
  $ conda activate PFGS

• Pytorch (Please first check your cuda version)

  $ conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.7 -c pytorch -c nvidia

• Other python packages: open3d, opencv-python, etc.

Gaussian Rasterization with High-dimensional Features

pip install ./submodules/diff-gaussian-rasterization

You can customize NUM_SEMANTIC_CHANNELS in submodules/diff-gaussian-rasterization/cuda_rasterizer/config.h for any number of feature dimensions that you want. ⭐ Thanks to the diff-gaussian-rasterization code from Feature-3DGS, which is a great help for our work.

Build third_party optionally

python build_pkg.py

Dataset

ScanNet:

• Download and extract data from the original ScanNet-V2 preprocess.

• Dataset structure:

  ── scannet
    └── scene0000_00
        ├── pose
              └──1.txt
         ├── intrinsic
              └──*.txt
        ├── color
               └──1.jpg
        └── scene0000_00_vh_clean_2.ply
        └── images.txt
    └── scene0000_01
  

• Pretrain

DTU:

• We reorganize the original datasets in our own format. Here we provide a demonstration of the test set of DTU, which can be downloaded here
• Pretrain

THuman2:

• Download 3D model and extract data from original THuman2.
• Render 36 views based on each 3D model and sparse sample points(8w) on the surface of the model by Blender.
• Demo and Pretrain

Train Stage 1

ScanNet:

python train_stage1.py --dataset scannet --scene_dir $data_path --exp_name scannet_stage1 --img_wh 640 512

DTU:

python train_stage1.py --dataset dtu --scene_dir $data_path --exp_name dtu_stage1 --img_wh 640 512

THuman2:

python train_stage1.py --dataset thuman2 --scene_dir $data_path --exp_name thuman2_stage1 --img_wh 512 512 --scale_max 0.0001

Train Stage 2

ScanNet:

python train_stage2.py --dataset scannet --scene_dir $data_path --exp_name scannet_stage2 --img_wh 640 512 --ckpt_stage1 $ckpt_stage1_path

DTU:

python train_stage2.py --dataset dtu --scene_dir $data_path --exp_name dtu_stage2 --img_wh 640 512 --ckpt_stage1 $ckpt_stage1_path

THuman2:

python train_stage1.py --dataset thuman2 --scene_dir $data_path --exp_name thuman2_stage1 --img_wh 512 512 --scale_max 0.0001 --ckpt_stage1 $ckpt_stage1_path

Eval

ScanNet:

python train_stage2.py --dataset scannet --scene_dir $data_path --exp_name scannet_stage2_eval --img_wh 640 512 --resume_path $ckpt_stage2_path --val_mode test

DTU:

python train_stage2.py --dataset dtu --scene_dir $data_path --exp_name dtu_stage2_eval --img_wh 640 512 --resume_path $ckpt_stage2_path --val_mode test

THuman2:

python train_stage1.py --dataset thuman2 --scene_dir $data_path --exp_name thuman2_stage1_eval --img_wh 512 512 --scale_max 0.0001 --resume_path $ckpt_stage2_path --val_mode test

The results will be saved in ./log/$exp_name

Acknowledgements

In this repository, we have used codes or datasets from the following repositories. We thank all the authors for sharing great codes or datasets.

• DTU
• ScanNet
• THuman2
• Trivol
• 3D Gaussian Splatting
• Feature-3DGS
• LION
• MIMO-UNet

Citation

@misc{wang2024pfgshighfidelitypoint,
      title={PFGS: High Fidelity Point Cloud Rendering via Feature Splatting}, 
      author={Jiaxu Wang and Ziyi Zhang and Junhao He and Renjing Xu},
      year={2024},
      eprint={2407.03857},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.03857}, 
}