  In recent years, the neural radiance field (NeRF) model has gained popularity
due to its ability to recover complex 3D scenes. Following its success, many
approaches proposed different NeRF representations in order to further improve
both runtime and performance. One such example is Triplane, in which NeRF is
represented using three 2D feature planes. This enables easily using existing
2D neural networks in this framework, e.g., to generate the three planes.
Despite its advantage, the triplane representation lagged behind in its 3D
recovery quality compared to NeRF solutions. In this work, we propose
TriNeRFLet, a 2D wavelet-based multiscale triplane representation for NeRF,
which closes the 3D recovery performance gap and is competitive with current
state-of-the-art methods. Building upon the triplane framework, we also propose
a novel super-resolution (SR) technique that combines a diffusion model with
TriNeRFLet for improving NeRF resolution.
