title: ProNeRF: Learning Efficient Projection-Aware Ray Sampling for
    Fine-Grained Implicit Neural Radiance Fields
summary: 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 10times
  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.
paper_authors._id: 657a75bff10a984150a495b6
paper_authors.name: Juan Luis Gonzalez Bello
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-18T08:48:22.582Z
paper_authors._id: 657ff1721815b29c9a1e485c
paper_authors.avatarUrl: /avatars/99143e93514f90c9dea282f182185941.svg
paper_authors.fullname: Juan Luis Gonzalez Bello
paper_authors.type: user
paper_authors.user: JuanLuisGB
paper_authors._id: 657a75bff10a984150a495b7
paper_authors.name: Minh-Quan Viet Bui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-22T14:29:01.484Z
paper_authors._id: 65859653c8b8bd66f7c5a651
paper_authors.avatarUrl: /avatars/84825a8224350dae85784534e9c59424.svg
paper_authors.fullname: Quan Bui
paper_authors.type: user
paper_authors.user: quan5609
paper_authors._id: 657a75bff10a984150a495b8
paper_authors.name: Munchurl Kim
paper_title: ProNeRF: Learning Efficient Projection-Aware Ray Sampling for
    Fine-Grained Implicit Neural Radiance Fields
paper_summary: 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 10times
  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.
paper_ai_keywords: implicit neural radiance fields sampler networks projection-aware sampling (PAS) network ray exploration and exploitation PSNR forward-facing datasets 360 datasets LLFF (Lund et al. FFrames) Blender