The codes have been tested on python 3.10, CUDA>=11.8. The simplest way to install all dependences is to use anaconda and pip in the following steps:

Adjust NUM_CHANNELS in /submodules/diff-gaussian-rasterization/cuda_rasterizer/config.h to set the number of weather variables for reconstruction.

conda create -n wea3dgs python=3.10 conda activate wea3dgs conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia git clone --recursive https://github.com/binbin2xs/weather-GS.git pip install -r requirements.txt 

Set the data path in /trainer/trainer.py (line 51).

For example: /dataset/era5_np_float32_part/2020/2020-01-01/00:00:00-t-1.0.npy represents:

Timestamp: 00:00:00 on 2020-01-01

Variable: Temperature (t) at pressure level 1.0

Shape: 721×1440

Single-GPU reconstruction command:

python run_weather_recon.py --sh_degree 0 --start_time [start_time(YYYY-MM-DD)] --end_time [end_time(YYYY-MM-DD)] 

Multi-GPU reconstruction command

bash run_weather_recon.sh or python run_weather_recon_parralel.py --gpus 0-15 --gl_start_time [start_time(YYYY-MM-DD)] --gl_end_time [end_time(YYYY-MM-DD)] 

If you want to directly render an image (.npy file) from a point cloud (.ply file), you can run the following command:

python render_weather.py 

Image Resolution: Adjust at Line 172&173.

Point Cloud Input Path: Set at Line 286.

Rendered Image Output Path: Set at Line 297.

Our recon is built upon 3DGS and CF3DGS. We thank all the authors for their great repos.