NRFF

Project page | Paper

This repository is an implementation of the view synthesis method described in the paper "Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis", CVPR 2023.

Kang Han¹, Wei Xiang²

¹James Cook University, ²La Trobe University

Abstract

Rendering novel views from captured multi-view images has made considerable progress since the emergence of the neural radiance field. This paper aims to further advance the quality of view synthesis by proposing a novel approach dubbed the neural radiance feature field (NRFF). We first propose a multiscale tensor decomposition scheme to organize learnable features so as to represent scenes from coarse to fine scales. We demonstrate many benefits of the proposed multiscale representation, including more accurate scene shape and appearance reconstruction, and faster convergence compared with the single-scale representation. Instead of encoding view directions to model view-dependent effects, we further propose to encode the rendering equation in the feature space by employing the anisotropic spherical Gaussian mixture predicted from the proposed multiscale representation. The proposed NRFF improves state-of-the-art rendering results by over 1 dB in PSNR on both the NeRF and NSVF synthetic datasets. A significant improvement has also been observed on the real-world Tanks & Temples dataset.

Installation

This implementation is based on PyTorch and TensoRF. You can create a virtual environment using Anaconda by running

conda create -n nrff python=3.8
conda activate nrff
pip3 install torch torchvision
pip3 install tqdm scikit-image opencv-python configargparse lpips imageio-ffmpeg kornia

Dataset

Please download one of the following datasets:

NeRF-synthetic

NSVF-synthetic

Tanks & Temples

Training

Specify the path of the data in configs/lego.txt and run

python train.py --config configs/lego.txt

Rendering

python train.py --config configs/lego.txt --ckpt path/to/your/checkpoint --render_only 1 --render_test 1 

Citation

If you find this code useful, please cite:

@inproceedings{han2023nrff,
    author={Han, Kang and Xiang, Wei},
    title={Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis},
    booktitle={The IEEE / CVF Computer Vision and Pattern Recognition Conference},
    pages={4232--4241},
    year={2023}
}

Acknowledgements

Thanks to the awesome neural rendering repositories of TensoRF and Instand-NGP.