Block-NeRF: Scalable Large Scene Neural View Synthesis

🤖 Plain-English Summary

We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. We add appearance embeddings, learned pose refinement, and controllable exposure to each individual NeRF, and introduce a procedure for aligning appearance between adjacent NeRFs so that they can be seamlessly combined.

🔑 Key Findings

• Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to de-compose the scene into individually trained NeRFs.
• This decomposition decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment.
• We adopt several architectural changes to make NeRF robust to data captured over months under different environmental conditions.

🔗 Related Resources