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.

翻译：最近神经渲染领域的进展表明，尽管速度较慢，但隐式紧凑模型能从多视角学习场景几何与视点相关外观。为保持这种小内存占用同时实现更快的推理速度，近期工作采用了"采样器"网络，在隐式神经辐射场中沿每条光线自适应采样少量点。虽然这些方法能将渲染时间缩短至原始NeRF的1/10，但其质量仍显著低于原始NeRF。相比之下，我们提出的ProNeRF在内存占用（与NeRF相当）、速度（优于HyperReel）和质量（优于K-Planes）之间达到了最优平衡。ProNeRF配备了一种新颖的投影感知采样（PAS）网络，并结合了新的光线探索与利用训练策略，实现了高效的细粒度粒子采样。我们的ProNeRF取得了最先进的指标：比NeRF快15-23倍且PSNR高0.65dB，比已发表的最佳基于采样的方法HyperReel高0.95dB。我们的探索与利用训练策略使ProNeRF不仅能学习完整场景的颜色与密度分布，还能聚焦于高密度区域进行高效光线采样。我们在广泛采用的前向与360度数据集（LLFF和Blender）上进行了大量实验，结果充分证明了我们方法的有效性。