  Neural Radiance Field (NeRF) approaches learn the underlying 3D
representation of a scene and generate photo-realistic novel views with high
fidelity. However, most proposed settings concentrate on modelling a single
object or a single level of a scene. However, in the real world, we may capture
a scene at multiple levels, resulting in a layered capture. For example,
tourists usually capture a monument's exterior structure before capturing the
inner structure. Modelling such scenes in 3D with seamless switching between
levels can drastically improve immersive experiences. However, most existing
techniques struggle in modelling such scenes. We propose Strata-NeRF, a single
neural radiance field that implicitly captures a scene with multiple levels.
Strata-NeRF achieves this by conditioning the NeRFs on Vector Quantized (VQ)
latent representations which allow sudden changes in scene structure. We
evaluate the effectiveness of our approach in multi-layered synthetic dataset
comprising diverse scenes and then further validate its generalization on the
real-world RealEstate10K dataset. We find that Strata-NeRF effectively captures
stratified scenes, minimizes artifacts, and synthesizes high-fidelity views
compared to existing approaches.
