We propose a novel Neural Radiance Field (NeRF) representation for non-opaque scenes that allows fast inference by utilizing textured polygons. Despite the high-quality novel view rendering that NeRF provides, a critical limitation is that it relies on volume rendering that can be computationally expensive and does not utilize the advancements in modern graphics hardware. Existing methods for this problem fall short when it comes to modelling volumetric effects as they rely purely on surface rendering. We thus propose to model the scene with polygons, which can then be used to obtain the quadrature points required to model volumetric effects, and also their opacity and colour from the texture. To obtain such polygonal mesh, we train a specialized field whose zero-crossings would correspond to the quadrature points when volume rendering, and perform marching cubes on this field. We then rasterize the polygons and utilize the fragment shaders to obtain the final colour image. Our method allows rendering on various devices and easy integration with existing graphics frameworks while keeping the benefits of volume rendering alive.

翻译：我们提出一种用于非透明场景的新型神经辐射场（NeRF）表示方法，该方法通过利用纹理化多边形实现快速推理。尽管NeRF提供了高质量的新视角渲染，但其依赖于体积渲染的关键局限性在于计算成本高昂且未能充分利用现代图形硬件的进步。针对这一问题的现有方法由于纯粹依赖表面渲染，在处理体积效应建模时存在不足。因此，我们提出使用多边形对场景进行建模，进而获取建模体积效应所需的正交点，并从纹理中提取其不透明度与颜色。为获得此类多边形网格，我们训练一个专用场，当执行体积渲染时，该场的零交叉点对应正交点，并对此场执行移动立方体算法。随后对多边形进行光栅化，并利用片段着色器生成最终彩色图像。我们的方法可在各类设备上进行渲染，并能够轻松集成至现有图形框架，同时保持体积渲染的优势。