The official code for "EfficientNeRF: Efficient Neural Radiance Fields" in CVPR2022.
Environment (Tested)
- Ubuntu 18.04
- Python 3.7
- CUDA 11.x
- Pytorch 1.9.1
- Pytorch-Lightning 1.6.4
Install via Anaconda
$ conda create -n EfficientNeRF python=3.8
$ conda activate EfficientNeRF
$ pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
$ pip install -r requirements.txt
Training
$ DATA_DIR=/path/to/lego
$ python train.py \
--dataset_name blender \
--root_dir $DATA_DIR \
--N_samples 128 \
--N_importance 5 --img_wh 800 800 \
--num_epochs 16 --batch_size 4096 \
--optimizer radam --lr 2e-3 \
--lr_scheduler poly \
--coord_scope 3.0 \
--warmup_step 5000\
--sigma_init 30.0 \
--weight_threashold 1e-5 \
--exp_name lego_coarse128_fine5_V384
Visualization
$ tensorboard --logdir=./logs
Question
- Q1. Different hyperparameters from the original paper
- A1. There are many combinations between these hyperparameters. You are free to balance the training speed and accuracy by modify them.
- Q2. When will NeRF-Tree released?
- A2. Hard to say a specific date. The data structure NeRF-Tree is closed to Octree.
Progress
More scenes and applications will be suported soon. Stay tune!
Acknowledgement
Our initial code was borrowed from
Citation
If you find our code or paper helps, please cite our paper:
@InProceedings{Hu_2022_CVPR,
author = {Hu, Tao and Liu, Shu and Chen, Yilun and Shen, Tiancheng and Jia, Jiaya},
title = {EfficientNeRF Efficient Neural Radiance Fields},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {12902-12911}
}