This repository contains a pytorch implementation for the paper: TensoRF: Tensorial Radiance Fields. Our work present a novel approach to model and reconstruct radiance fields, which achieves super
fast training process, compact memory footprint and state-of-the-art rendering quality.
train_process.mp4
Install environment:
conda create -n TensoRF python=3.8
conda activate TensoRF
pip install torch torchvision
pip install tqdm scikit-image opencv-python configargparse lpips imageio-ffmpeg
The training script is in train.py
, to train a TensoRF:
python train.py --config configs/lego.txt
we provide a few examples in the configuration folder, please note:
dataset_name
, choices = ['blender', 'llff', 'nsvf', 'dtu','tankstemple'];
shadingMode
, choices = ['MLP_Fea', 'SH'];
model_name
, choices = ['TensorVMSplit', 'TensorCP'], corresponding to the VM and CP decomposition;
n_lamb_sigma
and n_lamb_sh
are string type refer to the basis number of density and appearance along XYZ
dimension;
N_voxel_init
and N_voxel_final
control the resolution of matrix and vector;
N_vis
and vis_every
control the visualization during training;
You need to set --render_test 1
/--render_path 1
if you want to render testing views or path after training.
More options refer to the opt.py
.
https://1drv.ms/u/s!Ard0t_p4QWIMgQ2qSEAs7MUk8hVw?e=dc6hBm,
python train.py --config configs/lego.txt --ckpt path/to/your/checkpoint --render_only 1 --render_test 1
You can just simply pass --render_only 1
and --ckpt path/to/your/checkpoint
to render images from a pre-trained
checkpoint. You may also need to specify what you want to render, like --render_test 1
, --render_train 1
or --render_path 1
.
The rendering results are located in your checkpoint folder.
If you find our code or paper helps, please consider citing:
@misc{TensoRF,
title={TensoRF: Tensorial Radiance Fields},
author={Anpei Chen and Zexiang Xu and Andreas Geiger and and Jingyi Yu and Hao Su},
year={2022},
eprint={2203.09517},
archivePrefix={arXiv},
primaryClass={cs.CV}
}