Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. One reason is that they make use of volume rendering, thus requiring many samples (and model queries) per ray at render time. Although this representation is flexible and easy to optimize, most real-world objects can be modeled more efficiently with surfaces instead of volumes, requiring far fewer samples per ray. This observation has spurred considerable progress in surface representations such as signed distance functions, but these may struggle to model semi-opaque and thin structures. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces while modeling the (typically) small fraction of challenging regions volumetrically. We evaluate HybridNeRF against the challenging Eyeful Tower dataset along with other commonly used view synthesis datasets. When comparing to state-of-the-art baselines, including recent rasterization-based approaches, we improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2Kx2K).

翻译：神经辐射场提供了最先进的视图合成质量，但往往渲染速度较慢。原因之一是其采用体积渲染方法，在渲染时每条光线需要大量采样点（以及模型查询）。尽管这种表示方式灵活且易于优化，但现实世界中的大多数物体可以更高效地通过表面而非体积进行建模，每条光线所需的采样点数量显著减少。这一观察推动了基于符号距离函数等表面表示技术的显著进展，但此类方法在建模半透明和薄层结构时可能仍存在困难。我们提出HybridNeRF方法，通过将大多数物体渲染为表面，同时对（通常占比较小的）困难区域进行体积建模，从而融合两种表示的优势。我们在具有挑战性的Eyeful Tower数据集以及其他常用视图合成数据集上评估HybridNeRF。与包括近期基于光栅化方法的最先进基线相比，我们将误差率降低了15-30%，同时为虚拟现实分辨率（2K×2K）实现了实时帧率（至少36 FPS）。