  Neural Radiance Fields (NeRFs) have unmatched fidelity on large, real-world
scenes. A common approach for scaling NeRFs is to partition the scene into
regions, each of which is assigned its own parameters. When implemented
naively, such an approach is limited by poor test-time scaling and inconsistent
appearance and geometry. We instead propose InterNeRF, a novel architecture for
rendering a target view using a subset of the model's parameters. Our approach
enables out-of-core training and rendering, increasing total model capacity
with only a modest increase to training time. We demonstrate significant
improvements in multi-room scenes while remaining competitive on standard
benchmarks.
