Cited By
View all- Ma ZWu JNgo C(2024)Leveraging LLMs and Generative Models for Interactive Known-Item Video SearchMultiMedia Modeling10.1007/978-3-031-53302-0_35(380-386)Online publication date: 29-Jan-2024
ScaNeRF: Scalable Bundle-Adjusting Neural Radiance Fields for Large-Scale Scene Rendering
Authors: 
Xiuchao Wu, Jiamin Xu, Xin Zhang, Hujun Bao, Qixing Huang, Yujun Shen, 
James Tompkin, and Weiwei XuAuthors Info & Claims
Xiuchao Wu, Jiamin Xu, Xin Zhang, Hujun Bao, Qixing Huang, Yujun Shen, James Tompkin, and Weiwei XuAuthors Info & ClaimsArticle No.: 261, Pages 1 - 18
Abstract
High-quality large-scale scene rendering requires a scalable representation and accurate camera poses. This research combines tile-based hybrid neural fields with parallel distributive optimization to improve bundle-adjusting neural radiance fields. The proposed method scales with a divide-and-conquer strategy. We partition scenes into tiles, each with a multi-resolution hash feature grid and shallow chained diffuse and specular multilayer perceptrons (MLPs). Tiles unify foreground and background via a spatial contraction function that allows both distant objects in outdoor scenes and planar reflections as virtual images outside the tile. Decomposing appearance with the specular MLP allows a specular-aware warping loss to provide a second optimization path for camera poses. We apply the alternating direction method of multipliers (ADMM) to achieve consensus among camera poses while maintaining parallel tile optimization. Experimental results show that our method outperforms state-of-the-art neural scene rendering method quality by 5%--10% in PSNR, maintaining sharp distant objects and view-dependent reflections across six indoor and outdoor scenes.
Supplementary Material
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Index Terms
- ScaNeRF: Scalable Bundle-Adjusting Neural Radiance Fields for Large-Scale Scene Rendering
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Published: 05 December 2023
Published in TOG Volume 42, Issue 6
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View all- Ma ZWu JNgo C(2024)Leveraging LLMs and Generative Models for Interactive Known-Item Video SearchMultiMedia Modeling10.1007/978-3-031-53302-0_35(380-386)Online publication date: 29-Jan-2024
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