F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting
 
Here, we will explain how to use our codes.
Our codes is divided into two parts, one for synthetic-nerf dataset and other for Tanks&Temples dataset.


## We use the same environment as original 3DGS, please follow the link of 3D-GS to install all packages
https://github.com/graphdeco-inria/gaussian-splatting
Thanks for the excellent work in 3D-GS！


## Step 1.  Get hist path of original 3DGS

To use our code, first we need to use original 3DGS code to train each scene and get the .ply file for each scene. The distribution of original Gaussians will be used in next step.

## Step 2. Using the code in train.sh file to train synthetic-nerf and Tanks&Temples dataset

# In nerf-synthetic dataset
python train.py -s /workspace/datasets/nerf_synthetic/chair -m exp/chair   --eval --hist_path /gaussian-ori/gaussian-splatting/exp/chair/point_cloud/iteration_30000/point_cloud.ply   

-s           the source of dataset
--hist_path  the path of .ply file trained by original 3DGS
-m           the output of the model

# Tanks&Temples dataset
python train.py -s /workspace/datasets/TanksAndTemple/Barn  -m TanksAndTemple/Barn  --eval -r 2  -w --hist_path /gaussian-ori/gaussian-splatting/TanksAndTemple/Barn/point_cloud/iteration_30000/point_cloud.ply

-s           the source of dataset
--hist_path  the path of .ply file trained by original 3DGS
-m           the output of the model
-r           the resolution of images
-w           the background is white