Skip to content

Latest commit

 

History

History
43 lines (23 loc) · 1.79 KB

README.md

File metadata and controls

43 lines (23 loc) · 1.79 KB
Raw

instruct-nerf2nerf-gaussian-splatting

An attempt to see if the instruct-nerf2nerf idea works with 3D Gaussian Splatting.

Set up

Use conda environment for local development, and docker to build the Python wheels / run colmap. Benefits:

  • Can still use neovim for development and do not require remote-container support
  • Only need to install nvidia-driver, do not need to install CUDA in the system path

Credits go to authors of instruct-nerf2nerf and gaussian-splatting. I mostly ported and edited from their codebase.

Prepare Data

Download bear.zip from this google drive. And put it into data/ folder. Use colmap to get camera pose.

You can refer to run_colmap.sh for colmap commands.

How to Run

  1. Go to gaussian_splatting folder
  2. Run python train.py -s ../data/bear (where you put the data). It makes sure you can run gaussian splatting correctly
  3. Run python train_in2n.py -s ../data/bear. It will run gaussian splatting, and edit the image using instruct-pix2pix every 20 iterations.

Result @ 7000 iterations

Ground Truth

Rendered

Some findings:

  • A good instruct-pix2pix model is very important. During the optimization stage, I can see some edited images are very off..
  • The colors of tree leaves get brighter. Need more targeted edits in the diffusion model.
  • The idea of instruct-nerf2nerf is very cool!! I am surprised by the final results. Despite rendering at some angles can still generate weird images, it overall regularizes the editing pretty well.