  View synthesis methods using implicit continuous shape representations
learned from a set of images, such as the Neural Radiance Field (NeRF) method,
have gained increasing attention due to their high quality imagery and
scalability to high resolution. However, the heavy computation required by its
volumetric approach prevents NeRF from being useful in practice; minutes are
taken to render a single image of a few megapixels. Now, an image of a scene
can be rendered in a level-of-detail manner, so we posit that a complicated
region of the scene should be represented by a large neural network while a
small neural network is capable of encoding a simple region, enabling a balance
between efficiency and quality. Recursive-NeRF is our embodiment of this idea,
providing an efficient and adaptive rendering and training approach for NeRF.
The core of Recursive-NeRF learns uncertainties for query coordinates,
representing the quality of the predicted color and volumetric intensity at
each level. Only query coordinates with high uncertainties are forwarded to the
next level to a bigger neural network with a more powerful representational
capability. The final rendered image is a composition of results from neural
networks of all levels. Our evaluation on three public datasets shows that
Recursive-NeRF is more efficient than NeRF while providing state-of-the-art
quality. The code will be available at https://github.com/Gword/Recursive-NeRF.
