HourglassNeRF: Casting an Hourglass as a Bundle of Rays for Few-shot
  Neural Rendering
Seunghyeon Seo Yeonjin Chang Jayeon Yoo Seungwoo Lee Hojun Lee Nojun Kwak
Recent advancements in the Neural Radiance Field (NeRF) have bolstered its
capabilities for novel view synthesis, yet its reliance on dense multi-view
training images poses a practical challenge. Addressing this, we propose
HourglassNeRF, an effective regularization-based approach with a novel
hourglass casting strategy. Our proposed hourglass is conceptualized as a
bundle of additional rays within the area between the original input ray and
its corresponding reflection ray, by featurizing the conical frustum via
Integrated Positional Encoding (IPE). This design expands the coverage of
unseen views and enables an adaptive high-frequency regularization based on
target pixel photo-consistency. Furthermore, we propose luminance consistency
regularization based on the Lambertian assumption, which is known to be
effective for training a set of augmented rays under the few-shot setting.
Leveraging the inherent property of a Lambertian surface, which retains
consistent luminance irrespective of the viewing angle, we assume our proposed
hourglass as a collection of flipped diffuse reflection rays and enhance the
luminance consistency between the original input ray and its corresponding
hourglass, resulting in more physically grounded training framework and
performance improvement. Our HourglassNeRF outperforms its baseline and
achieves competitive results on multiple benchmarks with sharply rendered fine
details. The code will be available.