ManifoldNeRF: View-dependent Image Feature Supervision for Few-shot
  Neural Radiance Fields
Daiju Kanaoka Motoharu Sonogashira Hakaru Tamukoh Yasutomo Kawanishi
Novel view synthesis has recently made significant progress with the advent
of Neural Radiance Fields (NeRF). DietNeRF is an extension of NeRF that aims to
achieve this task from only a few images by introducing a new loss function for
unknown viewpoints with no input images. The loss function assumes that a
pre-trained feature extractor should output the same feature even if input
images are captured at different viewpoints since the images contain the same
object. However, while that assumption is ideal, in reality, it is known that
as viewpoints continuously change, also feature vectors continuously change.
Thus, the assumption can harm training. To avoid this harmful training, we
propose ManifoldNeRF, a method for supervising feature vectors at unknown
viewpoints using interpolated features from neighboring known viewpoints. Since
the method provides appropriate supervision for each unknown viewpoint by the
interpolated features, the volume representation is learned better than
DietNeRF. Experimental results show that the proposed method performs better
than others in a complex scene. We also experimented with several subsets of
viewpoints from a set of viewpoints and identified an effective set of
viewpoints for real environments. This provided a basic policy of viewpoint
patterns for real-world application. The code is available at
https://github.com/haganelego/ManifoldNeRF_BMVC2023