  Neural Radiance Field~(NeRF) achieves extremely high quality in object-scaled
and indoor scene reconstruction. However, there exist some challenges when
reconstructing large-scale scenes. MLP-based NeRFs suffer from limited network
capacity, while volume-based NeRFs are heavily memory-consuming when the scene
resolution increases. Recent approaches propose to geographically partition the
scene and learn each sub-region using an individual NeRF. Such partitioning
strategies help volume-based NeRF exceed the single GPU memory limit and scale
to larger scenes. However, this approach requires multiple background NeRF to
handle out-of-partition rays, which leads to redundancy of learning. Inspired
by the fact that the background of current partition is the foreground of
adjacent partition, we propose a scalable scene reconstruction method based on
joint Multi-resolution Hash Grids, named DistGrid. In this method, the scene is
divided into multiple closely-paved yet non-overlapped Axis-Aligned Bounding
Boxes, and a novel segmented volume rendering method is proposed to handle
cross-boundary rays, thereby eliminating the need for background NeRFs. The
experiments demonstrate that our method outperforms existing methods on all
evaluated large-scale scenes, and provides visually plausible scene
reconstruction. The scalability of our method on reconstruction quality is
further evaluated qualitatively and quantitatively.
