Block-NeRF: Scalable Large Scene Neural View Synthesis

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 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.
• 2 This decomposition decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment.
• 3 We adopt several architectural changes to make NeRF robust to data captured over months under different environmental conditions.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: