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', 'tankstemple'];
shadingMode, choices = ['MLP_Fea', 'SH'];
model_name, choices = ['TensorVMSplit', 'TensorCP'], corresponding to the VM and CP decomposition.
You need to uncomment the last a few rows of the configuration file if you want to training with the TensorCP model；
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}
}