ZZy129326999/Factorize-3DGS

F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting

Xiangyu Sun, Joo Chan Lee, Daniel Rho, Jong Hwan Ko, Usman Ali and Eunbyung Park

[Project Page] [Paper(arxiv)]

Our code is based on 3D Gaussian Splatting.

Setup

For installation, we use the same environment as 3D Gaussian Splatting.

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！

Training

Here, we will explain how to use our codes. Our codes are divided into two parts, one for synthetic-nerf dataset and other for Tanks&Temples dataset.

Pre-processing

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

Running

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

Refer to other arguments of 3DGS.