ProNeRF: Learning Efficient Projection-Aware Ray Sampling for
  Fine-Grained Implicit Neural Radiance Fields
Juan Luis Gonzalez Bello Minh-Quan Viet Bui Munchurl Kim
Recent advances in neural rendering have shown that, albeit slow, implicit
compact models can learn a scene's geometries and view-dependent appearances
from multiple views. To maintain such a small memory footprint but achieve
faster inference times, recent works have adopted `sampler' networks that
adaptively sample a small subset of points along each ray in the implicit
neural radiance fields. Although these methods achieve up to a 10$\times$
reduction in rendering time, they still suffer from considerable quality
degradation compared to the vanilla NeRF. In contrast, we propose ProNeRF,
which provides an optimal trade-off between memory footprint (similar to NeRF),
speed (faster than HyperReel), and quality (better than K-Planes). ProNeRF is
equipped with a novel projection-aware sampling (PAS) network together with a
new training strategy for ray exploration and exploitation, allowing for
efficient fine-grained particle sampling. Our ProNeRF yields state-of-the-art
metrics, being 15-23x faster with 0.65dB higher PSNR than NeRF and yielding
0.95dB higher PSNR than the best published sampler-based method, HyperReel. Our
exploration and exploitation training strategy allows ProNeRF to learn the full
scenes' color and density distributions while also learning efficient ray
sampling focused on the highest-density regions. We provide extensive
experimental results that support the effectiveness of our method on the widely
adopted forward-facing and 360 datasets, LLFF and Blender, respectively.