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 bundleadjusting neural radiance fields. The proposed method scales with a divideand-conquer strategy. We partition scenes into tiles, each with a multiresolution 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.

We address the bundle-adjusting NeRF problem for large-scale 3D scenes by combining a tile-based hybrid neural field scene representation with parallel distributive training via the alternating direction method of multipliers (ADMM).