Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D
  Scene Representations
Tristan Aumentado-Armstrong Ashkan Mirzaei Marcus A. Brubaker Jonathan Kelly Alex Levinshtein Konstantinos G. Derpanis Igor Gilitschenski
Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations,
capable of high quality novel view synthesis of complex scenes. While NeRFs
have been applied to graphics, vision, and robotics, problems with slow
rendering speed and characteristic visual artifacts prevent adoption in many
use cases. In this work, we investigate combining an autoencoder (AE) with a
NeRF, in which latent features (instead of colours) are rendered and then
convolutionally decoded. The resulting latent-space NeRF can produce novel
views with higher quality than standard colour-space NeRFs, as the AE can
correct certain visual artifacts, while rendering over three times faster. Our
work is orthogonal to other techniques for improving NeRF efficiency. Further,
we can control the tradeoff between efficiency and image quality by shrinking
the AE architecture, achieving over 13 times faster rendering with only a small
drop in performance. We hope that our approach can form the basis of an
efficient, yet high-fidelity, 3D scene representation for downstream tasks,
especially when retaining differentiability is useful, as in many robotics
scenarios requiring continual learning.