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This repository contains the official implementation of DietNeRF, a system that reconstructs 3D scenes from a few posed photos.
We use the following folder structure:
dietnerf/
logs/ (images, videos, checkpoints)
data/
nerf_synthetic/
configs/ (run configuration files)
CLIP/ (Fork of OpenAI's clip repository with a wrapper)
Create conda environment:
conda create -n dietnerf python=3.9
conda activate dietnerf
Set up requirements and our fork of CLIP:
pip install -r requirements.txt
cd CLIP
pip install -e .
Login to Weights & Biases:
wandb login
Realistic Synthetic experiments are implemented in the ./dietnerf subdirectory.
You need to download datasets from NeRF's Google Drive folder. The dataset was used in the original NeRF paper by Mildenhall et al. For example,
mkdir dietnerf/logs/ dietnerf/data/
cd dietnerf/data
pip install gdown
gdown --id 18JxhpWD-4ZmuFKLzKlAw-w5PpzZxXOcG -O nerf_synthetic.zip
unzip nerf_synthetic.zip
rm -r __MACOSX
Then, shrink images to 400x400:
python dietnerf/scripts/bulk_shrink_images.py "dietnerf/data/nerf_synthetic/*/*/*.png" dietnerf/data/nerf_synthetic_400_rgb/ True
These images are used for FID/KID computation. The dietnerf/run_nerf.py training and evaluation code automatically shrinks images with the --half_res argument.
Each experiment has a config file stored in dietnerf/configs/. Scripts in dietnerf/scripts/ can be run to train and evaluate models.
Run these scripts from ./dietnerf.
The scripts assume you are running one script at a time on a server with 8 NVIDIA GPUs.
cd dietnerf
export WANDB_ENTITY=<your wandb username>
# NeRF baselines
sh scripts/run_synthetic_nerf_100v.sh
sh scripts/run_synthetic_nerf_8v.sh
sh scripts/run_synthetic_simplified_nerf_8v.sh
# DietNeRF with 8 observed views
sh scripts/run_synthetic_dietnerf_8v.sh
sh scripts/run_synthetic_dietnerf_ft_8v.sh
# NeRF and DietNeRF with partial observability
sh scripts/run_synthetic_unseen_side_14v.sh
Coming soon. Our paper also fine-tunes pixelNeRF on DTU scenes for 1-shot view synthesis.
If DietNeRF is relevant to your project, please cite our associated paper:
@InProceedings{Jain_2021_ICCV,
author = {Jain, Ajay and Tancik, Matthew and Abbeel, Pieter},
title = {Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {5885-5894}
}
This code is based on Yen-Chen Lin's PyTorch implementation of NeRF and the official pixelNeRF code.