PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis
Abstract
This paper proposes a method for fast scene radiance field reconstruction with strong novel view synthesis performance and convenient scene editing functionality. The key idea is to fully utilize semantic parsing and primitive extraction for constraining and accelerating the radiance field reconstruction process. To fulfill this goal, a primitive-aware hybrid rendering strategy was proposed to enjoy the best of both volumetric and primitive rendering. We further contribute a reconstruction pipeline conducts primitive parsing and radiance field learning iteratively for each input frame which successfully fuses semantic, primitive, and radiance information into a single framework. Extensive evaluations demonstrate the fast reconstruction ability, high rendering quality, and convenient editing functionality of our method.
Cite
Text
Ying et al. "PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis." International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.01623Markdown
[Ying et al. "PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis." International Conference on Computer Vision, 2023.](https://mlanthology.org/iccv/2023/ying2023iccv-parf/) doi:10.1109/ICCV51070.2023.01623BibTeX
@inproceedings{ying2023iccv-parf,
title = {{PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis}},
author = {Ying, Haiyang and Jiang, Baowei and Zhang, Jinzhi and Xu, Di and Yu, Tao and Dai, Qionghai and Fang, Lu},
booktitle = {International Conference on Computer Vision},
year = {2023},
pages = {17706-17716},
doi = {10.1109/ICCV51070.2023.01623},
url = {https://mlanthology.org/iccv/2023/ying2023iccv-parf/}
}