@inproceedings{a6be1d532abf4bf7a4b3ed6174097e52,
title = "BoostMVSNeRFs: Boosting MVS-based NeRFs to Generalizable View Synthesis in Large-scale Scenes",
abstract = "While Neural Radiance Fields (NeRFs) have demonstrated exceptional quality, their protracted training duration remains a limitation. Generalizable and MVS-based NeRFs, although capable of mitigating training time, often incur tradeoffs in quality. This paper presents a novel approach called BoostMVSNeRFs to enhance the rendering quality of MVS-based NeRFs in large-scale scenes. We first identify limitations in MVS-based NeRF methods, such as restricted viewport coverage and artifacts due to limited input views. Then, we address these limitations by proposing a new method that selects and combines multiple cost volumes during volume rendering. Our method does not require training and can adapt to any MVS-based NeRF methods in a feed-forward fashion to improve rendering quality. Furthermore, our approach is also end-to-end trainable, allowing fine-tuning on specific scenes. We demonstrate the effectiveness of our method through experiments on large-scale datasets, showing significant rendering quality improvements in large-scale scenes and unbounded outdoor scenarios.",
keywords = "3D Synthesis, Neural Radiance Fields, Neural Rendering, Novel View Synthesis",
author = "Su, {Chih Hai} and Hu, {Chih Yao} and Tsai, {Shr Ruei} and Lee, {Jie Ying} and Lin, {Chin Yang} and Liu, {Yu Lun}",
note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; SIGGRAPH 2024 Conference Papers ; Conference date: 28-07-2024 Through 01-08-2024",
year = "2024",
month = jul,
day = "13",
doi = "10.1145/3641519.3657416",
language = "English",
series = "Proceedings - SIGGRAPH 2024 Conference Papers",
publisher = "Association for Computing Machinery, Inc",
editor = "Spencer, {Stephen N.}",
booktitle = "Proceedings - SIGGRAPH 2024 Conference Papers",
}