Neural radiance fields achieve unprecedented quality for novel view synthesis, but their volumetric formulation remains expensive, requiring a huge number of samples to render high-resolution images. Volumetric encodings are essential to represent fuzzy geometry such as foliage and hair, and they are well-suited for stochastic optimization. Yet, many scenes ultimately consist largely of solid surfaces which can be accurately rendered by a single sample per pixel. Based on this insight, we propose a neural radiance formulation that smoothly transitions between volumetric- and surface-based rendering, greatly accelerating rendering speed and even improving visual fidelity. Our method constructs an explicit mesh envelope which spatially bounds a neural volumetric representation. In solid regions, the envelope nearly converges to a surface and can often be rendered with a single sample. To this end, we generalize the NeuS formulation with a learned spatially-varying kernel size which encodes the spread of the density, fitting a wide kernel to volume-like regions and a tight kernel to surface-like regions. We then extract an explicit mesh of a narrow band around the surface, with width determined by the kernel size, and fine-tune the radiance field within this band. At inference time, we cast rays against the mesh and evaluate the radiance field only within the enclosed region, greatly reducing the number of samples required. Experiments show that our approach enables efficient rendering at very high fidelity. We also demonstrate that the extracted envelope enables downstream applications such as animation and simulation.

翻译：神经辐射场在新视角合成中达到了前所未有的质量，但其体积化表达方式依然代价高昂，需要耗费大量样本才能渲染高分辨率图像。体积编码对于表示树叶和毛发等模糊几何体至关重要，且非常适合随机优化。然而，许多场景本质上主要由固体表面构成，每个像素仅需单个样本即可精确渲染。基于这一洞察，我们提出了一种神经辐射公式，能够在体积渲染与表面渲染之间平滑过渡，大幅提升渲染速度，甚至改善视觉保真度。我们的方法构建了一个显式网格包络，从空间上约束神经体积表示。在固体区域中，该包络几乎收敛到表面，通常只需单个样本即可完成渲染。为此，我们推广了NeuS公式，引入了一个学习得到的空间变化核大小，用以编码密度的扩散程度：对类体积区域拟合宽核，对类表面区域拟合紧核。随后，我们提取出一个围绕表面的窄带显式网格，其宽度由核大小决定，并在此带内对辐射场进行微调。在推理时，我们针对该网格投射光线，并仅在包围区域内评估辐射场，从而大幅减少所需样本数量。实验表明，我们的方法能以极高保真度实现高效渲染。此外，我们证明提取出的包络还能支持动画和模拟等下游应用。