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This content will become publicly available on December 5, 2024

Title: ScaNeRF: Scalable Bundle-Adjusting Neural Radiance Fields for Large-Scale Scene Rendering

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.

 
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Award ID(s):
2047677
NSF-PAR ID:
10481158
Author(s) / Creator(s):
; ; ; ; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
ACM Transactions on Graphics
Volume:
42
Issue:
6
ISSN:
0730-0301
Page Range / eLocation ID:
1 to 18
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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